researchseminars.org

Prof Robert Griffiths (Monash University)
2020-04-30 01:00 - 02:00 UTC | https://stable.researchseminars.org/talk/StatisticsandStochastic/1/
Title: Lambda coalescent trees and graphs (https://stable.researchseminars.org/talk/StatisticsandStochastic/1/)
by Prof Robert Griffiths (Monash University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
The Lambda coalescent introduced by Pitman (1999) and Sagitov (1999) is a random tree which has multiple mergers. It is a dual to a Lambda-Fleming-Viot process which describes a population of individuals with births and deaths, where a single individual's children can contribute a large proportion of the population. The population process has jumps at times where individuals give birth. The Wright-Fisher diffusion in contrast, being a diffusion, is continuous over time. The Kingman coalescent, a random binary tree, is dual to the Wright-Fisher diffusion.

Dr Jessica Kasza (Monash University)
2020-05-07 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/2/
Title: Cross-overs, stepped wedges and staircases: some recent work in longitudinal cluster randomised trials (https://stable.researchseminars.org/talk/StatisticsandStochastic/2/)
by Dr Jessica Kasza (Monash University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Although individually randomised trials are the gold standard for assessing the impact of new treatments on patient outcomes, cluster randomised trials are necessary when testing the effect of healthcare provider-level changes on patient outcomes, e.g. the effect of a hospital-wide handwashing program on the number of patients who acquire infections in hospital. Cluster randomised trials often require large numbers of clusters and thus can be infeasible, but longitudinal cluster randomised trials, where clusters may switch between intervention and control, require smaller sample sizes. Cross-overs, stepped wedges and staircases are all particular variants of longitudinal cluster randomised trials that are being conducted with increasing frequency. However, many of the underlying statistical aspects of these designs remain under-explored.

Dr David Frazier (Monash University)
2020-06-11 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/3/
Title: Robust and Efficient Approximate Bayesian Computation: A Minimum Distance Approach (https://stable.researchseminars.org/talk/StatisticsandStochastic/3/)
by Dr David Frazier (Monash University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
In many instances, the application of approximate Bayesian methods is hampered by two practical features: 1) the requirement to project the data down to low-dimensional summary, including the choice of this projection, and which ultimately yields inefficient inference; 2) a possible lack of robustness of these methods to deviations from the underlying model structure. Motivated by these efficiency and robustness concerns, we construct a new Bayesian method that can deliver efficient estimators when the underlying model is well-specified, and which is simultaneously robust to certain forms of model misspecification. This new approach bypasses the calculation of summaries by considering a norm between empirical and simulated probability measures. For specific choices of the norm, we demonstrate that this approach can be as efficient as exact Bayesian inference, and is robust to deviations from the underlying model assumptions. We illustrate this approach using several examples that have featured in the literature.

Dr Damjan Vukcevic (University of Melbourne)
2020-06-18 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/4/
Title: Analysis of repeated categorical ratings: going beyond inter-rater agreement. (https://stable.researchseminars.org/talk/StatisticsandStochastic/4/)
by Dr Damjan Vukcevic (University of Melbourne) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
A common task in health and medicine is the classification of patient information into one of several categories by a trained expert. This could include assessing the presence and type of a tumour from a medical image or providing a disease diagnosis from a series of medical tests. Often such judgements are hard to make and error prone: two experts may rate the same scenario differently or the same expert may provide alternative ratings of the same scenario when rating it multiple times on different occasions.

Analysing the performance of such expert ‘raters’, and the accuracy of their ‘ratings’ across a series of ‘items’, is a common theme in much of the health and medical literature, especially in the setting where the true underlying category is unknown. Existing approaches, such as Cohen’s kappa, focus only on assessing inter-agreement, and have known problems stemming from the lack of any notion of underlying truth and the difficulty of coping with repeated ratings by the same rater.

Here we present and implement methods that explicitly model an underlying true category for each item and can cope naturally with any number of ratings for each item, including repeated ratings by the same rater. We implement Bayesian versions of these models using the probabilistic programming language Stan, and create an R package to fit and interrogate the output of these models.

Using real and simulated datasets, which are designed to mimic a wide range of medical scenarios, we test the performance of these models in estimating the true class of each item. We also explore situations such as having raters with much poorer accuracy, and comparisons with other (non-model-based) approaches.

A common task in health and medicine is the classification of patient information into one of several categories by a trained expert. This could include assessing the presence and type of a tumour from a medical image or providing a disease diagnosis from a series of medical tests. Often such judgements are hard to make and error prone: two experts may rate the same scenario differently or the same expert may provide alternative ratings of the same scenario when rating it multiple times on different occasions.

Analysing the performance of such expert ‘raters’, and the accuracy of their ‘ratings’ across a series of ‘items’, is a common theme in much of the health and medical literature, especially in the setting where the true underlying category is unknown. Existing approaches, such as Cohen’s kappa, focus only on assessing inter-agreement, and have known problems stemming from the lack of any notion of underlying truth and the difficulty of coping with repeated ratings by the same rater.

Here we present and implement methods that explicitly model an underlying true category for each item and can cope naturally with any number of ratings for each item, including repeated ratings by the same rater. We implement Bayesian versions of these models using the probabilistic programming language Stan, and create an R package to fit and interrogate the output of these models.

Using real and simulated datasets, which are designed to mimic a wide range of medical scenarios, we test the performance of these models in estimating the true class of each item. We also explore situations such as having raters with much poorer accuracy, and comparisons with other (non-model-based) approaches.

Dr Mumtaz Hussain (La Trobe University)
2020-06-25 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/5/
Title: Metric number theory via geometry and dynamics: Mahler to Margulis (https://stable.researchseminars.org/talk/StatisticsandStochastic/5/)
by Dr Mumtaz Hussain (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
There are two well-known approaches in solving the measure theoretic problems in Diophantine approximation. The metrical approach arise from the geometry of numbers and the ergodic theoretic approach arise from the dynamics on the space of lattices. One of the main ingredients in the geometry of numbers is the usage of Borel-Cantelli lemmas from probability theory. Dynamics on the space of lattices rely on the Dani correspondence principle (1985) which was extensively developed further by Margulis and Kleinbock. I will discuss both of these approaches and along the way discuss some well-known results such as the resolutions of Oppenheim (1929), Mahler (1932) and Sprindzuk (1965) conjectures which influenced my research in the last few years.

Marina Masioti (La Trobe University)
2020-06-03 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/6/
Title: Optimal transformations for dimension reduction and the problem of eigenvalue switching (https://stable.researchseminars.org/talk/StatisticsandStochastic/6/)
by Marina Masioti (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract: TBA

Jason Gavin Grealey (La Trobe University)
2020-03-19 04:00 - 05:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/7/
Title: Investigating the utility of neural networks in genomic prediction (https://stable.researchseminars.org/talk/StatisticsandStochastic/7/)
by Jason Gavin Grealey (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract: TBA

Illia Donhauzer (La Trobe University)
2020-09-04 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/8/
Title: Asymptotic behaviour of functionals of random fields (https://stable.researchseminars.org/talk/StatisticsandStochastic/8/)
by Illia Donhauzer (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
The talk is about the asymptotic behaviour of functionals of long-range dependent random fields. The Strong Law of Large Numbers (SLLN) and new properties of the limit processes in the Non-central Limit Theorem (NLT) will be discussed.

The SLLN for integral functionals of random fields with unboundedly increasing covariances will be presented. The SLLN is derived for the case of increasing domains. Conditions on covariance functions such that the SLLN holds will be provided. The considered scenarios include non-stationary random fields. The discussion about applications to weak and long-range dependent random fields and numerical examples will be shown.

New properties of generalized Hermite-type processes that arise in NLT for integral functionals of long-range dependent random fields will be demonstrated. Contrary to the classical one-dimensional case, it will be shown that for any choice of a multidimensional observation window the generalized Hermite-type process has non-stationary increments.

Dr Jie Yen Fan (Monash University)
2020-10-01 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/9/
Title: Multi-type age-structured population model (https://stable.researchseminars.org/talk/StatisticsandStochastic/9/)
by Dr Jie Yen Fan (Monash University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Population process in general setting, where each individual reproduces and dies depending on the state (such as age and type) of the individual as well as the entire population, offers a more realistic framework to population modelling. Formulating the population dynamics as a measure-valued stochastic process allows us to incorporate such dependence. We describe the dynamics of a multi-type age-structured population as a measure-valued process, and obtain its asymptotics, in particular, the law of large numbers and the central limit theorem.

Joint work with Kais Hamza, Peter Jagers and Fima Klebaner.

Mason Terrett (La Trobe University)
2020-10-01 03:05 - 04:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/10/
Title: SARGDV: Efficient identification of groundwater-dependent vegetation using synthetic aperture radar (https://stable.researchseminars.org/talk/StatisticsandStochastic/10/)
by Mason Terrett (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Groundwater depletion impacts the sustainability of numerous groundwater-dependent vegetation (GDV) globally, placing significant stress on their capacity to provide environmental and ecological support for flora, fauna, and anthropic benefits. Cost effective methods of GDV identification will enable strategic protection of these critical ecological systems, through improved and sustainable groundwater management by communities and industry. Recent application of synthetic aperture radar (SAR) earth observation data in Australia has demonstrated the utility of radar for identifying terrestrial groundwater-dependent ecosystems at scale. Our research included the development of SARGDV, a binary classification model, which uses the extreme gradient boosting (XGBoost) algorithm in conjunction with three data cubes composed of Sentinel-1 SAR interferometric wide images. Our method may be used to support the protection of GDV communities globally by providing a long term, cost-effective solution to identify GDVs over variable regions and climates, via the use of freely available, high-resolution, globally available Sentinel-1 SAR data sets. Our method offers global water management agencies a means toward more sustainable management of regional groundwater resources by providing an efficient method to identify significant GDV occurrence within areas where substantial groundwater extraction is ongoing.

Dr Tingjin Chu (University of Melbourne)
2020-10-15 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/11/
Title: Large Spatial Data Modeling and Analysis: A Krylov Subspace Approach (https://stable.researchseminars.org/talk/StatisticsandStochastic/11/)
by Dr Tingjin Chu (University of Melbourne) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Estimating the parameters of spatial models for large spatial datasets can be computationally challenging, as it involves repeated evaluation of sizable spatial covariance matrices. In this paper, we aim to develop Krylov subspace based methods that are computationally efficient for large spatial data. Specifically, we approximate the inverse and the log-determinant of the spatial covariance matrix in the log-likelihood function via conjugate gradient and stochastic Lanczos on a Krylov subspace. These methods reduce the computational complexity from $O(N^3)$ to $O(N^2)$ and $O(N\log N)$ for dense and sparse matrices, respectively. Moreover, we quantify the difference between the approximated log-likelihood function and the original log-likelihood function and establish the consistency of parameter estimates. Simulation studies are conducted to examine the computational efficiency as well as the finite-sample properties. For illustration, our methodology is applied to analyze a large LiDAR dataset.

This is joint work with Jialuo Liu, Jun Zhu and Haonan Wang.

4 presenters (La Trobe University)
2020-11-05 23:00 - 2020-11-06 01:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/12/
Title: Theses talks by 4 students (https://stable.researchseminars.org/talk/StatisticsandStochastic/12/)
by 4 presenters (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Presented by:

10.00am Vibhooti Bhatnagar. A comparison of AIC and nested t-tests for nested model selection.

10.25am Navdeep Kaur. Is corrected AIC really better than AIC?

10.50am Satbir Kaur Bansal. Visualization of Variability of AIC.

11.15 Ravindra Nath Dahal. A review of Prediction Intervals obtained from model free machine learning algorithms for point prediction

Prof Gael M. Martin (Monash University)
2020-11-18 23:30 - 2020-11-19 00:30 | https://stable.researchseminars.org/talk/StatisticsandStochastic/13/
Title: Computing Bayes: Bayesian Computation from 1763 to the 21st Century (https://stable.researchseminars.org/talk/StatisticsandStochastic/13/)
by Prof Gael M. Martin (Monash University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characterize Bayesian inference, with the rules of probability used to transform prior probability distributions for all unknowns - models, parameters, latent variables - into posterior distributions, subsequent to the observation of data. Conducting Bayesian inference requires the evaluation of integrals in which these probability distributions appear. Bayesian computation is all about evaluating such integrals in the typical case where no analytical solution exists. This paper takes the reader on a chronological tour of Bayesian computation over the past two and a half centuries. Beginning with the one-dimensional integral first confronted by Bayes in 1763, through to recent problems in which the unknowns number in the millions, we place all computational problems into a common framework, and describe all computational methods using a common notation. The aim is to help new researchers in particular - and more generally those interested in adopting a Bayesian approach to empirical work - make sense of the plethora of computational techniques that are now on offer; understand when and why different methods are useful; and see the links that do exist, between them all.

Joint results with David T. Frazier (Monash University) and Christian P. Robert (University of Dauphine, Paris). The paper appears as an arXiv pre-print. We are revising it at the moment, but it won't change in its essence: https://arxiv.org/pdf/2004.06425.pdf

Ravindi Nanayakkara (La Trobe University)
2020-11-19 03:00 - 04:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/14/
Title: Stochastic Modelling and Statistical Analysis of Dependent Data (https://stable.researchseminars.org/talk/StatisticsandStochastic/14/)
by Ravindi Nanayakkara (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
First, we discuss the obtained results about the analysis of spherical monofractal and multifractal random fields with cosmological applications. The Rényi function plays an important role in the analysis of multifractal random fields. For random fields on the sphere, there are three models in the literature where the Rényi function is known explicitly [1]. The main statistical model used to describe CMB data in the literature is isotropic Gaussian fields. We present some new theoretical models, numerical multifractality studies and methodology based on simulating random fields, computing the Rényi function and the multifractal spectrum for different scenarios and actual CMB data. The results suggest that there may exist a very minor multifractality of the CMB data [2].

Next, we discuss the obtained results about the asymptotic normality of simultaneous estimators of cyclic long-memory processes. Spectral singularities at non-zero frequencies play an important role in investigating cyclic or seasonal time series. The publication [3] introduced the generalized filtered method-of-moments approach to simultaneously estimate singularity location and long-memory parameters. This study [4] continues investigations of these simultaneous estimators. The results about asymptotic normality of several statistics are obtained. The methodology includes wavelet transformations as a particular case. The theoretical findings are illustrated by numerical results including Meyer, Shannon father wavelets and Mexican hat wavelets.

Finally, we discuss multifractionality of spherical random fields with cosmological applications. The Hölder exponent is used to measure the roughness in a rigorous mathematical way [5]. In this study, one dimensional and two dimensional pointwise Hölder exponent values are computed for the CMB data using the HEALPix ring ordering and nested ordering visualisations. The results suggest that there exist a considerable multifractionality in CMB data.

References:

Leonenko, N. & Shieh, N.R. (2013). Rényi function for multifractal random fields. Fractals, 21(2), 1350009.
Leonenko, N., Nanayakkara, R., & Olenko, A. (2020). Analysis of Spherical Monofractal and Multifractal Random Fields. Stochastic Environmental Research and Risk Assessment Journal. https://doi.org/10.1007/s00477-020-01911-z
Alomari, H. M., Ayache, A., Fradon, M. & Olenko, A. (2020). Estimation of cyclic long-memory parameters. Scandinavian Journal of Statistics, 47(1) 104-133.
Ayache, A., Fradon, M., Nanayakkara, R., & Olenko, A. (2020). Asymptotic normality of simultaneous estimators of cyclic long-memory processes. Submitted.
Ayache, A., & Véhel, J. L. (2004). On the identification of the pointwise Hölder exponent of the generalized multifractional Brownian motion. Stochastic Processes and their Applications, 111(1), 119–56.

Nishika Ranathunga (La Trobe University)
2020-12-09 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/15/
Title: Confidence Intervals in General Regression Models that Utilize Uncertain Prior Information (https://stable.researchseminars.org/talk/StatisticsandStochastic/15/)
by Nishika Ranathunga (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
We consider a general regression model, without a scale parameter. We construct a confidence interval for a scalar parameter of interest that utilizes the uncertain prior information that a distinct scalar parameter takes the specified value. This confidence interval has good coverage properties. It also has scaled expected length, where the scaling is with respect to the usual confidence interval, that is (a) substantially less than 1 when the prior information is correct, (b) has a maximum value that is not too large and (c) is close to 1 when the data and prior information are highly discordant.

Furthermore, in Kabaila and Ranathunga (2020), we solve the problem of numerically evaluating the expected value of a smooth bounded function of a chi-distributed random variable, divided by the square root of the number of degrees of freedom, using Mori's transformation followed by the trapezoidal rule, which is exponentially convergent for suitable integrands. This problem arises in simultaneous inference, selection and ranking of populations, the evaluation of multivariate t probabilities and the assessment of coverage and expected volume properties of non-standard confidence regions.

We apply this solution in the R package ciuupi2 that computes the Kabaila and Giri (2009) confidence interval, which utilizes the uncertain prior information in a linear regression model with unknown error variance. Previous computations of this interval used MATLAB programs that were time-consuming to run. By writing these programs in R, the computation time is greatly reduced and they become freely available. We also assess a new definition of scaled expected length.

Finally, we compare the computations of the log-likelihood function for generalized linear mixed models using (a) adaptive Gauss-Hermite quadrature and (b) importance sampling, where both methods share the same initial step (Kabaila and Ranathunga, 2019).

References:

Kabaila, P., & Giri, K. (2009). Confidence intervals in regression utilizing prior information. Journal of Statistical Planning and Inference, 139, 3419-3429.
Kabaila P. and Ranathunga N. (2019) On Adaptive Gauss-Hermite Quadrature for Estimation in GLMM’s. In: Nguyen H. (eds) Statistics and Data Science. RSSDS 2019. Communications in Computer and Information Science, vol 1150. Springer, Singapore.
Kabaila, P., & Ranathunga, N. (2020). Computation of the expected value of a chi-distributed random variable. Computational Statistics.

Jason Grealey (La Trobe University, Baker Institute)
2021-03-12 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/16/
Title: Quantifying computational carbon footprints and deep learning in genomic prediction (https://stable.researchseminars.org/talk/StatisticsandStochastic/16/)
by Jason Grealey (La Trobe University, Baker Institute) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This presentation details the three main projects undertaken within my PhD. The first involves investigating the carbon footprint of computation. As climate change is an extremely pressing global issue, researchers must be prudent with energy usage, this includes computational research. In this first project we developed a freely available and simple to use carbon footprint estimator of computational tools called Green Algorithms, it provides interpretable metrics to understand any given carbon footprint. The next section I will talk about involves the estimation of the carbon footprints of various bioinformatic analyses using published benchmarks. These carbon footprints are largely unknown and underappreciated within the research community, we also provide a list of realistic and practical recommendations that computational researchers can utilise in order to minimise their carbon footprint. The last section is a simulation study aiming to understand what types of genetic architectures and study designs are needed to utilise neural networks in place of traditional linear polygenic scoring methods in genomic prediction.

Nicholas Sing (La Trobe University, Baker Institute)
2021-04-01 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/17/
Title: Mining Lipidomics for Biological Insight (https://stable.researchseminars.org/talk/StatisticsandStochastic/17/)
by Nicholas Sing (La Trobe University, Baker Institute) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Lipidomics is the study of all lipids that make up cells and organisms. Abnormal lipid metabolism is associated with many cardiovascular risk factors. The Baker Heart and Diabetes Institute has generated lipidomic datasets for several population studies. These datasets can contain hundreds of lipid species and sample numbers ranging from hundreds to several thousands. During lipidomic analysis unwanted variation can arise due to variation from technical sources, which unwanted variation removal algorithms aim to minimise. This project aims to develop multivariate methodologies for dealing with unwanted variation in lipidomic datasets and modelling the metabolic associations between groups of lipid species and participant characteristics. We intend to use eigenlipids to explore the existence, onset or progression of metabolic disease. We have demonstrated that eigenlipids can outperform many individual lipid species in predicting cardiovascular risk factors. To identify technical sources of unwanted variation in the plasma lipidome during laboratory processing we recently performed a laboratory experiment, which will support the utilisation of unwanted variation removal algorithms for removing variation from laboratory processing in pre-existing datasets.

Mitra Jazayeri (La Trobe University)
2021-05-06 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/18/
Title: Factors affecting first year psychology students’ statistics learning (https://stable.researchseminars.org/talk/StatisticsandStochastic/18/)
by Mitra Jazayeri (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Teaching statistics to different disciplines is increasingly challenging. This is due to several factors including the wide range of students’ academic backgrounds, availability of data and public perception of its importance. In addition, advancements in technology and recent technological innovations in teaching also present challenges due to the large gap between learning theory and teaching practices.
Furthermore, experiencing anxiety when studying statistics, as a prerequisite subject, has always been commonplace for students around the world. Statistics anxiety can appear as a complex array of emotional reactions from only a minor discomfort to severe forms of apprehension, fear, nervousness, panic and worry. Considering that statistics is often required as a core subject in a wide range of university degrees, research into assisting in overcoming these challenges is essential.
This research aims to explore intervention methods to minimize students’ apprehension in their learning process. This is presented in three parts: 1) an examination of the effect of blended delivery of an introductory statistics subject, 2) a systematic review investigating interventions utilized to reduce students’ statistics anxiety, 3a) the introduction of a survey tool for evaluation of student attitudes, confidence, anxiety, and beliefs about the usefulness of learning statistics in their degree and an assessment of its’ reliability and validity, 3b) design, implementation and analysis of a web-based mindfulness intervention delivered to a sample of 530 students studying statistics for psychology during COVID-19 era. This project will help inform educators for the better delivery of statistics to students with diverse academic backgrounds.

A/Prof Minh-Ngoc Tran (University of Sydney)
2021-06-17 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/19/
Title: Variational Bayes on Manifolds (https://stable.researchseminars.org/talk/StatisticsandStochastic/19/)
by A/Prof Minh-Ngoc Tran (University of Sydney) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. Nonetheless, the development of the existing VB algorithms is so far generally restricted to the case where the variational parameter space is Euclidean, which hinders the potential broad application of VB methods. This paper extends the scope of VB to the case where the variational parameter space is a Riemannian manifold. We develop an efficient manifold-based VB algorithm that exploits both the geometric structure of the constraint parameter space and the information geometry of the manifold of VB approximating probability distributions. Our algorithm is provably convergent and achieves a decent convergence rate. We develop in particular several manifold VB algorithms including Manifold Gaussian VB and Stiefel Neural Network VB, and demonstrate through numerical experiments that the proposed algorithms are stable, less sensitive to initialization and compares favourably to existing VB methods. This is a joint work with Dang Nguyen and Duy Nguyen.

Masters Students (La Trobe University)
2021-06-24 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/20/
Title: Masters Students Talks (https://stable.researchseminars.org/talk/StatisticsandStochastic/20/)
by Masters Students (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract: TBA

Dr Matias Quiroz (University of Technology Sydney)
2021-08-19 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/21/
Title: Spectral Subsampling MCMC for Stationary Multivariate Time Series (https://stable.researchseminars.org/talk/StatisticsandStochastic/21/)
by Dr Matias Quiroz (University of Technology Sydney) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Spectral subsampling MCMC was recently proposed to speed up Markov chain Monte Carlo (MCMC) for long stationary univariate time series by subsampling periodogram observations in the frequency domain. This talk presents an extension of the approach to stationary multivariate time series. We also propose a multivariate generalisation of the autoregressive tempered fractionally differentiated moving average model (ARTFIMA). The new model is shown to provide a better fit compared to multivariate autoregressive moving average models for three real world examples. We demonstrate that spectral subsampling may provide up to two orders of magnitude faster estimation, while retaining MCMC sampling efficiency and accuracy, compared to spectral methods using the full dataset.

Prof Christopher Drovandi (Queensland University of Technology)
2021-09-09 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/22/
Title: Statistical Inference for Implicit Models using Bayesian Synthetic Likelihood (https://stable.researchseminars.org/talk/StatisticsandStochastic/22/)
by Prof Christopher Drovandi (Queensland University of Technology) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Implicit models are defined as those that can be simulated but the associated likelihood function is intractable. Such models are prevalent in many fields such as biology, ecology, cosmology and epidemiology. Given the unavailability of the likelihood function, statistical inference for implicit models is challenging as we must rely only on the ability to generate mock datasets from the model of interest, and compare it with the observed data in some way. This talk will explain a useful method called Bayesian synthetic likelihood for conducting such statistical inference. I will discuss how BSL can be extended to reduce the number of model simulations required and to make it more robust to model misspecification. I will also describe some theoretical properties of the method.

Illia Donhauzer (La Trobe University)
2021-09-16 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/23/
Title: On the asymptotic behavior of functionals of random fields (https://stable.researchseminars.org/talk/StatisticsandStochastic/23/)
by Illia Donhauzer (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
The talk is about the asymptotic behavior of functionals of random fields with possible long-range dependence. New properties of generalized Hermite-type processes, the Strong Law of Large Numbers (SLLN) for random fields, and the asymptotic behavior of running maxima of random double arrays will be discussed.

New properties of generalized Hermite-type processes that arise in NLT for integral functionals of long-range dependent random

fields will be demonstrated. Contrary to the classical one-dimensional case, it will be shown that for any choice of a multidimensional observation window the generalized Hermite-type process has non-stationary increments.

The SLLN for integral functionals of random fields with unboundedly increasing covariances will be presented. The SLLN is derived for the case of increasing domains. Conditions on covariance functions such that the SLLN holds will be provided. The considered scenarios include non-stationary random fields. The discussion about applications to weak and long-range dependent random fields and numerical examples will be shown.

Results on the asymptotic behavior of running maxima functionals of random double arrays of phi-subgaussian random variables will be demonstrated. The main results are specified for various important particular scenarios and classes of phi-subgaussian random variables.

Dr. Miryana Grigorova (University of Leeds)
2021-09-30 09:00 - 10:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/24/
Title: Superhedging of options in a non-linear incomplete financial market model. (https://stable.researchseminars.org/talk/StatisticsandStochastic/24/)
by Dr. Miryana Grigorova (University of Leeds) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
We will study the superhedging price (and superhedging strategies) of European and American options in a non-linear incomplete market model with default, with a particular focus on the American options case which is more involved. We will provide a dual representation of the seller’s (superhedging) price for the American option in terms of a mixed stochastic control/stopping problem with non-linear expectations/ evaluations, and in terms of non-linear Reflected BSDEs with constraints. If time permits, we will also present a duality result for the buyer’s price in terms of a stochastic game of control and stopping with non-linear expectations/ evaluations.

Zoom meeting link:

https://unimelb.zoom.us/j/86951431269?pwd=[REDACTED]

(if the link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

Dr. Francis K.C. Hui (Australian National University)
2021-10-21 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/25/
Title: Spatio-temporal joint species distribution modeling – A community-level basis function approach (https://stable.researchseminars.org/talk/StatisticsandStochastic/25/)
by Dr. Francis K.C. Hui (Australian National University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
The last decade in ecology has seen the development and rising popularity of joint species distribution modeling
approaches for studying species assemblages, with by far the most common approach being based around
generalized linear latent variable models (LVMs). However, while methodological and computational advances
continue to be made with LVMs, their application to spatio-temporal multivariate abundance data i.e., observations
of multiple species recorded across space and/or time, remains computationally challenging and not necessarily
scalable when it comes to fitting and inference.

In this talk, we propose an alternative approach to spatio-temporal joint species distribution modeling which breaks
away from the LVM framework. Inspired by the concept of fixed rank kriging, we employ a set of fixed, communitylevel
spatial and/or temporal basis functions, with corresponding species-specific random slopes to account for
spatio-temporal correlations both within and between species. The resulting community-level basis function model
(CBFM) can be used for the same array of purposes as LVMs, but is designed to be computationally much more
efficient given they can be set up and thus fitted using the same machinery as for generalized additive models.
Simulations and an application to a demersals fish dataset collected off the Northeast US continental shelf illustrate
the potential of CBFMs for scalable spatio-temporal joint species distribution modeling.

Masters Students (La Trobe University)
2021-11-04 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/26/

Mitra Jazayeri (La Trobe University)
2021-12-16 04:00 - 05:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/27/
Title: Validity analysis of a modified extended Technology Acceptance Model for first year Psychology students (https://stable.researchseminars.org/talk/StatisticsandStochastic/27/)
by Mitra Jazayeri (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This talk continues on from my confirmation where an outline of my research and the results for the first two phases of my project were given. In this talk I predominantly present the steps taken to evaluate the survey tool, which is a modified and extended Technology Acceptance Model (TAM). This measurement scale determines the perception of psychology students about the ease of use and usefulness of statistical concepts and their application in psychology using the statistical software, SPSS.

The proposed model was tested for its reliability and structural validity using data collected from a survey of first year psychology students studying statistics during the global pandemic in 2020. To explore the structure of the constructs of students’ attitude, confidence and perception, an Exploratory Factor Analysis (EFA) was conducted on the responses data set. Five latent variables were identified. Utilizing maximum likelihood estimates in Confirmatory Factor Analysis (CFA), and Analysis of Moment Structures (AMOS), the results supported the proposed EFA model. In addition, results of the CFA indicated that the best fitted model had correlations among four of the five constructs. Internal consistency estimates utilizing alpha coefficients, ranged from 0.81 to 0.88 with only one exception of 0.682. The findings provide a valid and reliable assessment of students’ attitudes towards statistics for predicting academic performance. Consequently, this may help as a guide for effective decision-making in the design and development of the statistics subjects for students with a non-mathematical background.

Yeshna Bhowon (La Trobe Universty)
2022-02-23 22:30 - 23:30 | https://stable.researchseminars.org/talk/StatisticsandStochastic/28/
Title: Applications Of Data Science Methods Within A Community-Based Physical Activity Program For Young People With Disability (https://stable.researchseminars.org/talk/StatisticsandStochastic/28/)
by Yeshna Bhowon (La Trobe Universty) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
FitSkills is a community-based program that connects university student mentors to young people living with disability through exercise programs at their local community gyms. Access to exercise facilities is a commonly documented perceived barrier to participation in physical activity for people living with disability, but the problem has not been quantified. We conducted a geospatial analysis using a population cohort in an aim to quantify this perceived barrier. The second part of my research used data collected during the FitSkills trial to determine if completing FitSkills fostered positive attitudes towards disability among the student mentors.

Nicholas Sing (La Trobe University and Baker Heart and Diabetes Institute)
2022-04-07 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/29/
Title: Mining Lipidomics for Biological Insight (https://stable.researchseminars.org/talk/StatisticsandStochastic/29/)
by Nicholas Sing (La Trobe University and Baker Heart and Diabetes Institute) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Lipidomics is the study of all lipids that make up cells and organisms. Abnormal lipid metabolism is associated with many cardiovascular risk factors. The Baker Heart and Diabetes Institute has generated lipidomic datasets for several population studies. During lipidomic analysis unwanted variation can arise due to variation in laboratory processing and handling, which unwanted variation removal algorithms aim to minimise. This project aims to develop multivariate methodologies for dealing with unwanted variation in lipidomic datasets and modelling the metabolic associations between groups of lipid species and participant characteristics. In this project we are using lipid set enrichment analysis and eigenlipids to explore lipid biology associated with cardiovascular disease. To identify technical sources of unwanted variation in the plasma lipidome during laboratory processing we performed a laboratory experiment and utilised the Remove Unwanted Variation-III (RUV-III) algorithm to remove these sources of unwanted variation from the lipidomic dataset we acquired. We intend to use this as a basis to identify negative control lipids to remove similar sources of unwanted variation in population lipidomic datasets using RUV-III.

Dr Alan Huang (University of Queensland)
2022-04-28 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/30/
Title: On arbitrarily underdispersed discrete distributions (https://stable.researchseminars.org/talk/StatisticsandStochastic/30/)
by Dr Alan Huang (University of Queensland) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
We review a range of generalized count distributions, investigating which (if any) can be arbitrarily underdispersed, i.e., its variance can be arbitrarily small compared to its mean. A philosophical implication is that models failing this criterion perhaps should not be considered a “statistical model” according to the extendibility criterion of McCullagh (2002). Four practical implications will be discussed. We suggest that all generalizations of the Poisson distribution be tested against this property.

Ayesha Perera (La Trobe Univerity)
2022-06-16 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/31/
Title: Performance of Model Averaged Tail Area Confidence Interval (https://stable.researchseminars.org/talk/StatisticsandStochastic/31/)
by Ayesha Perera (La Trobe Univerity) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Every model has an uncertainty in the variables that it should include. Model averaging is considered as a promising method that could be used to perform inference in the presence of model uncertainty. The performance of this method heavily depends on the data-based model weights used. Traditionally, this weight is chosen to be proportional to the exponential of minus the Generalized Information Criterion divided by two. We observe that the model-based confidence interval performs better, in terms of coverage and expected length, in the case of two nested linear regression models when this division by two is replaced by multiplied by a positive tuning constant. In the second part of the talk, we extend the analysis of the performance of Model Averaged Tail Area confidence interval by Kabaila, Welsh and Abeysekara, Scandinavian Journal of Statistics, 2016, to the case of three or more nested linear regression models. We also assess the influence of the weight function on the performance of this confidence interval for three nested linear regression models applied to the ‘Cholesterol’ data set.

Masters students talks (La Trobe Univerity)
2022-06-23 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/32/
Title: 2 masters theses presentation (https://stable.researchseminars.org/talk/StatisticsandStochastic/32/)
by Masters students talks (La Trobe Univerity) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract: TBA

Faraz Fattah Hesari (La Trobe Univerity)
2022-06-30 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/33/
Title: Statistical modelling of property prices, applying genetic algorithm and isolation forests (https://stable.researchseminars.org/talk/StatisticsandStochastic/33/)
by Faraz Fattah Hesari (La Trobe Univerity) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract: TBA

Dr Boris Buchmann (Australian National University)
2022-08-04 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/34/
Title: Weak Subordination of Multivariate Levy Processes (https://stable.researchseminars.org/talk/StatisticsandStochastic/34/)
by Dr Boris Buchmann (Australian National University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
You are welcome to attend the following Statistics and Stochastic colloquium (part of the Colloquium Series of the Department of Mathematics and Statistics) at La Trobe Universit, which is jointly organized with the Probability Victoria Seminar.

PVSeminar #36, Thursday 04 August / 17:00 AEST

Boris Buchmann (Australian National University, Australia): Weak Subordination of Multivariate Levy Processes

Abstract: Subordination is the operation which evaluates a Levy process at a subordinator, giving rise to a pathwise construction of a "time-changed" process. Originating with Bochner in the context of probability semigroups, subordination was applied by Madan and Seneta to create the variance gamma process, which is prominently used in financial modelling. However, unless the subordinate has independent components or the subordinator has indistinguishable components, subordination may not produce a Levy process.

We introduce a new operation known as weak subordination that always produces a Levy process by assigning the distribution of the subordinate conditional on the value of the subordinator, and matches traditional subordination in law in the cases above. Weak subordination is applied to extend the class of variance generalised gamma convolutions and to construct the weak variance-alpha-gamma process. The latter process exhibits a wider range of dependence than using traditional subordination.

Joint work with Kevin W Lu (UW), Dilip B Madan (UM), Marcus Michaelsen (UHH), Adam Nie (NTU), Alex Szimayer (UHH).

Zoom meeting link: https://unimelb.zoom.us/j/83757047993?pwd=[REDACTED]

(if the link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

A PDF file with the talk slides might become available for downloading from our seminar Webpage at https://probvic.wordpress.com/pvseminar/ prior to the talk (the above Zoom is being posted there).

Prof Yimin Xiao (Michigan State University, USA)
2022-08-18 00:00 - 01:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/35/
Title: Sample Path and Extreme Value Properties of Multivariate Gaussian Random Fields (https://stable.researchseminars.org/talk/StatisticsandStochastic/35/)
by Prof Yimin Xiao (Michigan State University, USA) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
In this talk, we present some recent results on sample path and extreme value properties of a large class of multivariate Gaussian random fields including multivariate Gaussian fields, operator fractional Brownian motion, vector-valued operator-scaling random fields, and matrix-valued Gaussian random fields. These results illustrate explicitly the effects of the dependence structures among the coordinate processes on the sample path and extreme value properties of multivariate Gaussian random fields.

Zoom meeting link: https://unimelb.zoom.us/j/86460269383?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

A PDF file with the talk slides might become available for downloading from our seminar Webpage at https://probvic.wordpress.com/pvseminar/ prior to the talk (the above Zoom link has already been posted there).

Prof Giovanni Peccati (University of Luxembourg)
2022-09-08 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/36/
Title: Some variations on a theme by P. de Jong (https://stable.researchseminars.org/talk/StatisticsandStochastic/36/)
by Prof Giovanni Peccati (University of Luxembourg) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Joint La Trobe statistics and stochastics and PV seminar

Abstract: In a remarkable paper from 1990, the Dutch mathematician P. de Jong proved a striking Central Limit Theorem yielding that, for a sequence of normalized and degenerate U-statistics verifying a Lindeberg-type condition, convergence to Gaussian is equivalent to the convergence of their fourth cumulants to zero. Such a result is the ancestor of the collection of “fourth-moment theorems” for non-linear functionals of random fields, that have recently played a prominent role in several questions of mathematical physics and stochastic geometry. In my talk, I will first present some quantitative multidimensional extensions of de Jong’s result, obtained by using Stein’s method of exchangeable pairs. I will then discuss some recent functional versions of de Jong’s findings, both in the symmetric and non-symmetric cases. The results in the symmetric case yield some novel universality results for U-processes, generalizing a classic invariance principle by Miller and Sen (1972), and allowing one to establish a complete taxonomy of functional CLTs associated with counting statistics of random geometric graphs. My presentation is mainly based on the following references:

Ch. Döbler and G. Peccati: Quantitative de Jong Theorems in any dimension. EJP, 2016.

Ch. Döbler, M. Kasprzak and G. Peccati: Weak convergence of U-processes with size-dependent kernels. Ann. App. Prob., 2022

Ch. Döbler, M. Kasprzak and G. Peccati. The multivariate functional de Jong CLT. Probab. Th. Rel. Fields, 2022+

Zoom meeting link: https://unimelb.zoom.us/j/82317899187?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

A PDF file with the talk slides might become available for downloading from our seminar Webpage at https://probvic.wordpress.com/pvseminar/ prior to the talk (the above Zoom link will also be posted there shortly).

Honours and Masters students (La Trobe University)
2022-10-27 01:00 - 02:30 | https://stable.researchseminars.org/talk/StatisticsandStochastic/37/
Title: Honours and Masters theses presentations (https://stable.researchseminars.org/talk/StatisticsandStochastic/37/)
by Honours and Masters students (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
The schedule is the following (20min for Thesis B talks and 10min for Thesis A talk):

12.05pm Adam Bilchouris. Investigating Statistical Properties of Functionals of Strongly Dependent Spatial Data.

12.15pm Lennon Zachary Logan. Mathematics of Kirigami. A Study of Euclidean Nets and Hyperbolic Crystals.

12.35pm Dmytro Ostapenko. Statistical Modelling of ANZ Property Data.

12.55pm Juliet Nwabuzor. Performance of Preliminary Model Selection Using Bayesian Information Criterion (Bic).

Mitra Jazayeri (La Trobe university)
2022-12-14 23:30 - 2022-12-15 00:30 | https://stable.researchseminars.org/talk/StatisticsandStochastic/38/
Title: Teaching statistics in a new era- factors affecting students’ learning and aligning technology with how we teach (https://stable.researchseminars.org/talk/StatisticsandStochastic/38/)
by Mitra Jazayeri (La Trobe university) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This thesis investigates the factors that affect psychology students’ ability to learn statistics in a new era of technological advancements and the effects post-Covid on education. Historically, teaching statistics to psychology students has been one of the most challenging tasks for statistics educators worldwide. Previous research has been conducted around the theme of social science students’ statistics anxiety and the varied survey designs developed to measure it. However, little has been done to reduce statistics anxiety, with the aim of increasing the performance of students with a non-mathematical background, particularly in this technological age.

The aim of this research is to i) conduct multiple regression and sub-group analyses using the R software package to investigate whether the blended delivery of a 12-week statistics subject to first-year psychology students had any effect on performance compared to face-to-face teaching only; ii) design a mindfulness intervention, together with a step-by-step methodological approach for teaching statistics to first-year psychology students; iii) develop a survey based on the technology acceptance model to measure students’ anxiety which included testing the validity and reliability of the adopted survey tool. To do so, the structural properties of the survey were investigated. For this stage of the research, jamovi and the IBM SPSS AMOS software package were utilized to obtain Cronbach’s alpha and the exploratory and confirmatory factor analysis output. This thesis finds that the web-based mindfulness intervention had a significant positive effect on students’ statistics anxiety and therefore performance. Moreover, five constructs were identified which affect students’ statistics anxiety and therefore their performance, namely attitude, confidence, student’s awareness of their mental state, independent learner belief, and dependent learner belief. The findings of this research may assist and inspire statistics educators internationally in their approach to the design and development of their teaching material to non-mathematical students for whom statistics is a core subject in their study.

Prof. Gennady Samorodnitsky (Cornell University)
2023-03-22 23:00 - 2023-03-23 00:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/39/
Title: Clustering of large deviations in moving average processes: short and long memory regimes (https://stable.researchseminars.org/talk/StatisticsandStochastic/39/)
by Prof. Gennady Samorodnitsky (Cornell University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
You are welcome to attend the following Statistics and Stochastic colloquium (part of the Colloquium Series of the Department of Mathematics and Statistics) at La Trobe University organised together with the PVSeminar (please note the UNUSUAL time).

++++++++++++++++++

PVSeminar #44, Thursday 23 March / 10:00 AEDT

Gennady Samorodnitsky (Cornell University, United States of America): Clustering of large deviations in moving average processes: short and long memory regimes

Abstract: We describe the cluster of large deviations events that arise when one such large deviations event occurs. We work in the framework of an infinite moving average process with a noise that has finite exponential moments. The cluster turns out to have different shapes in the cases when the moving average process has short memory and long memory.

Joint work with Arijit Chakrabarty.

Zoom meeting link:

https://unimelb.zoom.us/j/88379660402?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

A PDF file with the talk slides might become available for downloading from our seminar Webpage at https://probvic.wordpress.com/pvseminar/ prior to the talk (the above Zoom link is already there).

Prof. V.S. Matveev (Die Friedrich-Schiller-Universität Jena, the Federal Republic of Germany // La Trobe University, the Commonwealth of Australia)
2023-05-11 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/40/
Title: Geodesic random walks, diffusion processes, and Brownian motion on Finsler manifolds (https://stable.researchseminars.org/talk/StatisticsandStochastic/40/)
by Prof. V.S. Matveev (Die Friedrich-Schiller-Universität Jena, the Federal Republic of Germany // La Trobe University, the Commonwealth of Australia) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
We show that geodesic random walks on a complete Finsler manifold of bounded geometry converge to a diffusion process which is, up to a drift, the Brownian motion corresponding to a Riemannian metric. In particular, the generator of the limit process is a non-degenerate elliptic second-order partial differential operator for which we give a precise integral formula. If the geodesic random walk is geometric, that is if the law of increments are constructed by the Finsler metric by a coordinate-invariant procedure, the Riemannian metric is then determined by the Finsler metric. Special cases of such functors F → g_F include the Binet-Legendre metric and different average metrics and has many effective applications in Finsler geometry in which in particular certain mathematicians from the La Trobe University are involved. Also, possible applications in natural and life sciences will be discussed.

Most results are joint with Tianyu Ma and Ilya Pavlyukevich.

Zoom meeting link:

https://unimelb.zoom.us/j/86301899265?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

A PDF file with the talk slides (if any) might become available for downloading from the Webpage at https://probvic.wordpress.com/pvseminar/ prior to the talk.

Prof Ben Goldys (The University of Sydney, the Commonwealth of Australia)
2023-05-25 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/41/
Title: Stochastic flows in infinite dimensions (https://stable.researchseminars.org/talk/StatisticsandStochastic/41/)
by Prof Ben Goldys (The University of Sydney, the Commonwealth of Australia) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Stochastic flows associated with finite dimensional SDEs and random dynamical systems are an important tool for the study of large time behaviour of solutions and for their pathwise analysis. The existence of (smooth) stochastic flows in finite dimensions is well understood, see for example books by Kunita. The famous Skorokhod example shows that the situation is completely different in infinite dimensions (hence for stochastic PDEs). The existence of the flow for infinite dimensional stochastic systems is known in some special cases but is little understood. We will present new results on the existence of the flows in infinite dimensions. We will also present applications to the existence theory of stochastic PDEs and to the question of regularity of transition semigroups.

Zoom meeting link:

https://unimelb.zoom.us/j/81406950751?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

Nicholas Sing (La Trobe University)
2023-06-01 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/42/
Title: Mining Lipidomics for Biological Insight (https://stable.researchseminars.org/talk/StatisticsandStochastic/42/)
by Nicholas Sing (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Lipidomics is the study of all lipids that make up cells and organisms. Abnormal lipid metabolism is associated with many cardiovascular risk factors. Past lipidomic studies have lacked appropriate cohort sizes to explore bioinformatics methodologies used for dealing with large cohorts. The Baker Heart and Diabetes Institute has uniquely generated lipidomic datasets for large population cohort studies. During lipidomic analysis unwanted variation can arise due to variation in laboratory processing and handling, which unwanted variation removal algorithms aim to minimise. To identify technical sources of unwanted variation in the plasma lipidome during laboratory processing we performed a laboratory experiment and utilised the Removing Unwanted Variation-III (RUV-III) algorithm to remove these sources of unwanted variation from (1) the lipidomic dataset we acquired and (2) these same sources of unwanted variation in a large population cohort study generated via the Baker Heart and Diabetes Institute’s Lipidomic’s platform. We then focus on modelling the metabolic associations between groups of lipid species and cardiovascular disease. We specifically, used lipid set enrichment analysis and eigenlipids to explore lipid biology associated with cardiovascular disease using the lipidomic datasets generated for two large population cohorts via the Baker Heart and Diabetes Institute’s Lipidomics platform.

Join from a PC, Mac, iOS or Android: https://latrobe.zoom.us/j/87608535816

Or iPhone one-tap (Australia Toll): +61280152088,87608535816#

Or Telephone:
Dial: +61 2 8015 2088
Meeting ID: 876 0853 5816
International numbers available: https://latrobe.zoom.us/u/kNXgIF2zg

Or a H.323/SIP room system:
Dial: 87608535816@zoom.aarnet.edu.au
or 87608535816@zmau.us
or 103.122.166.55
Meeting ID: 87608535816

Or Skype for Business (Lync):
SIP:87608535816@lync.zoom.us

Honours and Masters students (La Trobe University)
2023-06-15 02:00 - 03:30 | https://stable.researchseminars.org/talk/StatisticsandStochastic/43/
Title: Honours and Masters theses presentations (https://stable.researchseminars.org/talk/StatisticsandStochastic/43/)
by Honours and Masters students (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
The schedule is the following (20min for Thesis B talks and 10min for Thesis A talk):

12:00 Sanjanaa Roopa Sunil. Modelling the impact of the increase in human lifespan on the emergence of infectious diseases.

12:15 Adam Brown-Sarre. Metrical Theory of Continued Fractions.

12:40 Adam Bilchouris. Methodologies for Exploring Out-of-Sample Prediction and Spatial Dependency for Complex Big Data.

13:05 Nazmi Amir. Generalised Entropy Indices for Analysis of Customers/Experts Opinion.

Prof. Antoine Ayache (Lille University, France)
2023-08-08 03:00 - 04:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/44/
Title: Harmonizable Fractional Stable Motion: simultaneous estimators for both parameters (https://stable.researchseminars.org/talk/StatisticsandStochastic/44/)
by Prof. Antoine Ayache (Lille University, France) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
There are two classical very different extensions of the well-known Gaussian fractional Brownian motion to non-Gaussian frameworks of heavy-tailed stable distributions:
the harmonizable fractional stable motion (HFSM) and the linear fractional stable motion (LFSM). As far as we know, while several articles in the literature, some of which
appeared a long time ago, have proposed statistical estimators for the parameters of LFSM, no estimator has yet been proposed in the framework of HFSM. Among other
things, what makes statistical estimation of parameters of HFSM to be a difficult problem is that, in contrast to LFSM, HFSM is not ergodic. The main goal of our talk is to
propose a new strategy for dealing with this problem and obtaining solutions of it. The keystone of our new strategy consists in the construction of new transforms of HFSM
which allow to obtain, at any dyadic level, a sequence of independent stable random variables.

Maha Alghamdi (La Trobe University)
2023-09-07 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/45/
Title: Properties of Functionals of Random Fields with Applications to Spatial Data Analysis (https://stable.researchseminars.org/talk/StatisticsandStochastic/45/)
by Maha Alghamdi (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
In this presentation, I will overview the theory of stochastic processes and random fields. I will discuss the historical development and key results of limit theorems for non-linear functionals of random fields. Definitions of Hermite expansions, semi-stable random fields and slowly varying functions will be presented. Additionally, I will highlight a recent new result in the field of limit theorems for Gaussian spatial processes, specifically the Spectral Central Limit Theorem for Functionals of Isotropic and Stationary Gaussian Fields. The plan for the future research will be presented.

Prof Ivan Nourdin (University of Luxembourg)
2023-11-16 07:30 - 08:30 | https://stable.researchseminars.org/talk/StatisticsandStochastic/46/
Title: Quantitative CLTs in Deep Neural Networks (https://stable.researchseminars.org/talk/StatisticsandStochastic/46/)
by Prof Ivan Nourdin (University of Luxembourg) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Abstract: In this talk, we will study the distribution of a fully connected neural network with random Gaussian weights and biases in which the hidden layer widths are proportional to a large constant n. More precisely, we will explain how to prove quantitative bounds on normal approximations valid at large but finite n and any fixed network depth. This is based on a joint work with S. Favaro, B. Hanin, D. Marinucci and G. Peccati.

Zoom meeting link:

https://unimelb.zoom.us/j/82073536928?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

A PDF file with the talk slides (if any) might become available for downloading from our seminar web page at https://probvic.wordpress.com/pvseminar/ prior to the talk (the above Zoom link has already been posted there).

Masters students (La Trobe University)
2023-11-16 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/47/
Title: Theses talks (https://stable.researchseminars.org/talk/StatisticsandStochastic/47/)
by Masters students (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Masters theses presentations:

12:00 Sanjanaa Roopa Sunil. How might the change in human longevity have affected the emergence and transmission of infectious diseases (Thesis B)

12:25 Sal Sabila. Modelling and Analysis of Property Prices Using Their Features (Thesis A)

Nemini Samarakoon (La Trobe University)
2023-11-22 07:30 - 08:30 | https://stable.researchseminars.org/talk/StatisticsandStochastic/48/
Title: Construction, Properties and Simulation of Haar-Based Multifractional Processes (https://stable.researchseminars.org/talk/StatisticsandStochastic/48/)
by Nemini Samarakoon (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract: TBA

Adam Bilchouris (La Trobe University)
2024-04-11 02:00 - 02:35 | https://stable.researchseminars.org/talk/StatisticsandStochastic/49/
Title: Methods for Exploring Out-of-Sample Prediction and Spatial Dependency for Complex Big Data (https://stable.researchseminars.org/talk/StatisticsandStochastic/49/)
by Adam Bilchouris (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
In this presentation, estimators of stochastic processes and isotropic Gaussian random fields will be considered, in particular, the covariance functions of these objects. The parameters of a Gegenbauer process will be estimated through its spectral density, and for isotropic Gaussian random fields, the normality of the first Minkowski functional is tested. Several existing covariance function estimators will be introduced, and their properties will be discussed. New estimation techniques will be introduced for isotropic Gaussian random fields which were developed to handle denser and larger data sets (i.e. big data). Simulation studies were conducted in order to determine the effectiveness of the above estimators under varying degrees of dependencies in the data.

Shahid Khan (La Trobe University)
2024-04-11 02:35 - 03:10 | https://stable.researchseminars.org/talk/StatisticsandStochastic/50/
Title: Spherical Stochastic Models and Approximation Schemes (https://stable.researchseminars.org/talk/StatisticsandStochastic/50/)
by Shahid Khan (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
In numerous applications it is important to model and analyse spatial data on a sphere. A common assumption is that spatial covariance functions exhibit isotropy. While existing approaches in the literature focus on Euclidean spaces, they are not suitable for spherical domains. Utilizing spherical harmonic (SH) representation, we formulate a test for isotropy. Data is projected onto spherical harmonic functions which form orthogonal basis functions on the sphere. We then exploit the fact that if the process is isotropic, the correlation between the coefficients will be zero. This motivates a test based on the sample correlation matrix of SH coefficients, using the largest eigenvalue as the test statistic. We test our method on simulated and Cosmic Microwave Background (CMB) radiation data by selecting random regions and using CMB radiation data from them.

Jayamini Chamindira Liyanage (La Trobe University)
2024-03-06 04:00 - 04:40 | https://stable.researchseminars.org/talk/StatisticsandStochastic/51/
Title: Multivariate Meta-Analysis Methods for High-Dimensional Data (https://stable.researchseminars.org/talk/StatisticsandStochastic/51/)
by Jayamini Chamindira Liyanage (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract: TBA

Dr Selvaraaju Murugesan (SaaS startup Kovai.co)
2024-04-23 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/52/
Title: Recent Trends in Data Science and AI (https://stable.researchseminars.org/talk/StatisticsandStochastic/52/)
by Dr Selvaraaju Murugesan (SaaS startup Kovai.co) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Artificial Intelligence technology is now becoming ubiquitous. This technology landscape is shifting rapidly given the rise of GenAI based apps such as ChatGPT, Gemini and so on. This talk will introduce students to recent trends in data science practice and Artificial Intelligence. The key takeaways would be
a. Understand the importance of data and its business use case
b. Know trends in AI
c. Skillsets required to be a data scientist
d. Building GenAI apps

A/Prof Simon Harris (University of Auckland)
2024-05-02 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/53/
Title: Genealogies of samples from stochastic population models (https://stable.researchseminars.org/talk/StatisticsandStochastic/53/)
by A/Prof Simon Harris (University of Auckland) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
You are welcome to attend the following Statistics and Stochastic colloquium (part of the Colloquium Series of the Department of Mathematics and Statistics) at La Trobe University organised together with the PVSeminar (please note the UNUSUAL time).

++++++++++++++++++

What does the family tree look like for a random sample of k individuals taken from some population? Surprisingly, until relatively recently this fundamental question remained an open problem even for sampling extant individuals in one of the simplest of stochastic population models. We will discuss some recent progress in this area, including the emergence of certain universal limiting genealogies when sampling individuals uniformly at random at large times from large stochastically evolving populations, such as near-critical Bienyame-Galton-Watson stochastic branching processes conditioned to survive. Whilst these universal genealogical trees have the same tree topology as a Kingman coalescent, it turns out that their coalescent (or split) times are quite different due to stochastic population size effects, although with an explicit representation as some mixture of IID split times. We will mention how these universal genealogies are intimately related to sampling from the Brownian excursion and Aldous’ Continuum Random Tree. Some ongoing research and open problems will also be discussed.

This talk is related to various collaborations with S.Bocharov (Xi’an Jiaotong-Liverpool), S.Johnston (KCL), J.C.Pardo (CIMAT), S.Palau (UNAM), and M.Roberts (Bath). We also acknowledge the support of the New Zealand Royal Society Te Apārangi Marsden fund.

Zoom meeting link:

https://unimelb.zoom.us/j/89920990824?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

Prof D. Marinucci (University of Rome “Tor Vergata”)
2024-05-23 06:00 - 07:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/54/
Title: Geometry and topology of spin random fields (https://stable.researchseminars.org/talk/StatisticsandStochastic/54/)
by Prof D. Marinucci (University of Rome “Tor Vergata”) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This event is being organized in collaboration with the PVSeminar.

Abstract: The investigation of the geometric properties of the excursion sets for isotropic random fields has been the object of a considerable amount of research over the last decade. In this talk, we discuss how to generalize some of these results to the case of random fields which are not scalar valued, but they are rather random sections of a non-trivial fiber bundle: these are the so-called spin (spherical) random fields, which play a key role in Cosmology, especially in connection with the analysis of Cosmic Microwave Background (CMB) polarization. In particular, we discuss how to characterize their expected geometry and topology: i.e., we investigate the asymptotic behaviour, under scaling assumptions, of general classes of functionals of (properly defined) excursion sets, including Lipschitz-Killing Curvatures and Betti Numbers.

Based on joint works with Antonio Lerario, Maurizia Rossi and Michele Stecconi.

Zoom meeting link:

https://unimelb.zoom.us/j/85717338005?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

Prof Enzo Orsingher (L'Università degli Studi di Roma "La Sapienza")
2024-05-09 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/55/
Title: Planar random motions at finite velocity (https://stable.researchseminars.org/talk/StatisticsandStochastic/55/)
by Prof Enzo Orsingher (L'Università degli Studi di Roma "La Sapienza") as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This event is being organized in collaboration with the PVSeminar.

Abstract: In our talk, we examine various models of motion in the plane with a finite or an infinite number of directions, The case of finite directions includes cyclic motions and clockwise or counterclockwise motions when from a direction d_k one can pass either to direction d_(k+1) or d_(k-1) with equal or different probabilities. Particular attention is paid to the planar motion with orthogonal directions. The switches of directions are governed by a homogeneous or non-homogeneous Poisson process, because in this case explicit results can be obtained. Planar motions with an infinite number of directions are performed at velocity c with changes occurring at Poisson-paced times and each new direction is taken with a uniformly distributed angle. The minimal case (three directions) is also considered and its connection with higher-order Bessel functions is presented.

Some of the results are published in: Fabrizio Cinque and Enzo Orsingher, Stochastic Dynamics of Generalized Planar Random Motions with Orthogonal directions, J. Theor. Probab. 2023, 36(4), pp. 2229-2261, and in the references of this paper,

Zoom meeting link:

https://unimelb.zoom.us/j/82613441430?pwd=[REDACTED]

(if the above link doesn't work when you click it -- please copy & paste it into the address bar in your browser).

Password: [REDACTED] (just in case)

Prof Günter Last (Karlsruher Institut für Technologie)
2024-06-13 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/56/
Title: Cluster density and uniqueness of the infinite cluster of the random connection (https://stable.researchseminars.org/talk/StatisticsandStochastic/56/)
by Prof Günter Last (Karlsruher Institut für Technologie) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
We consider a random connection model (RCM) on a general space driven by a Poisson process whose intensity measure is scaled by a parameter $t\ge 0$. An important special case is the stationary marked RCM (in Euclidean space), containing the Boolean model with general compact grains and the so-called weighted RCM as special cases. We say that the infinite clusters are deletion stable if the removal of a Poisson point cannot split a cluster in two or more infinite clusters. We prove that this stability together with a natural irreducibility assumption implies uniqueness of the infinite cluster. We then show that the infinite clusters of the stationary marked RCM are deletion stable. It follows that an irreducible stationary marked RCM can have at most one infinite cluster which extends and unifies several results in the literature. An important ingredient of our proofs are differentiability and convexity properties of the cluster density which are of interest in their own right.

The talk is based on recent joint work with Mikhail Chebunin: https://arxiv.org/abs/2403.17762. Some of the main ideas come from a seminal paper by Aizenman, Kesten and Newman (1987), treating discrete percolation models.

joint La Trobe Stochastics and Statistics and PV seminar

Masters and Honours students
2024-06-20 01:00 - 04:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/57/
Title: Masters and Honours theses talks (https://stable.researchseminars.org/talk/StatisticsandStochastic/57/)
by Masters and Honours students as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
11:00 Lauren White. Continued Fractions Restricted to Prime Partial Quotients.
11:15 Sigfrido Ciletti. Zero-One Laws for Intuitionistic Logic.
11:40 Sal Sabila. Modeling and Analysis of Property Prices Using Their Features.
12:05 Tracey Anderson. Pseudorandom Generation of Convex Lattice Polygons.
12:30 Qian Ding. Ranking With Dominance Matrices: Dominance Quantification and Weighting Methods.
12:55 Mohammed Mohsin Ghori. Hypergraphs and Lotka-Volterra Systems.
13:20 Ryan Nahkuri. Methods for Combining P-Values: A Comparison and a Tool for Researchers.

Maha Alghamdi (La Trobe University)
2024-09-12 06:00 - 07:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/58/
Title: Multiscaling limit theorems for stochastic FPDE with cyclic long-range dependence (https://stable.researchseminars.org/talk/StatisticsandStochastic/58/)
by Maha Alghamdi (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
The talk will discuss solutions of stochastic partial differential equations with random initial conditions. First, it overviews some of the known results on scaled solutions of such equations and provides several explicit examples. Then, it presents multiscaling limit theorems for renormalized solutions for the case of initial conditions subordinated to the random processes with cyclic long-range dependence. Two cases of stochastic partial differential equations are examined. The spectral and covariance representations for the corresponding limit random fields are obtained. Additionally, we will discussed why analogous results are not valid for subordinated cases with Hermite ranks greater than 1. Numerical examples that illustrate the obtained theoretical results will presented.

Master students (La Trobe University)
2024-11-14 01:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/59/
Title: Thesis talks (https://stable.researchseminars.org/talk/StatisticsandStochastic/59/)
by Master students (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Lauren White, Continued Fractions Restricted to Prime Partial Quotients
Ryan Nahkuri, Methods for Combining P-Values: A Comparison and a Tool for Researchers
Tracey Anderson, Pseudorandom Generation of Convex Lattice Polygons
Mohammed Mohsin Ghori, Hypergraphs and Lotka-Volterra Systems
Qian Ding, Ranking With Dominance Matrices: Dominance Quantification and Weighting Methods

Nemini Yadeesha Samarakoon (La Trobe University)
2025-01-31 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/60/
Title: Construction, Properties and Simulation of Haar-Based Multifractional Processes with Rmultifractional R package (https://stable.researchseminars.org/talk/StatisticsandStochastic/60/)
by Nemini Yadeesha Samarakoon (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Multifractional processes were introduced in the mid-1990s, as the fractional Brownian motion (fBm) could not model changes in the roughness of trajectories over time. The constant Hurst parameter of fBm was replaced by a time-varying Hurst function. The talk will introduce a new class of multifractional processes, the Gaussian Haar-based multifractional processes (GHBMP) which is based on the Haar wavelet approach. GHBMP provides a theoretical model and simulation tools for a wider class of processes and Hurst functions. The theoretical properties of the GHBMP will also be discussed. Additionally, Rmultifractional R package will be introduced. This R package includes functions for the simulation of Gaussian Haar-based multifractional processes, estimation of the Hurst function and analysis of multifractional processes.

Shahid Khan (La Trobe University)
2025-05-01 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/61/
Title: Exploring the Structure of Anisotropic Random Fields on the Sphere (https://stable.researchseminars.org/talk/StatisticsandStochastic/61/)
by Shahid Khan (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This talk explores fundamental questions about the existence of isotropic and anisotropic spherical fields and the behaviour of isotropic fields under composite transformations. It identifies feasible and infeasible scenarios, highlighting differences from random fields in Euclidean spaces. Connections to the spectral representations of spherical random fields are discussed. The results offer guidance for developing models and sampling strategies for parameter estimation and hypothesis testing of spherical data.

Masters and Honors students (La Trobe University)
2025-06-19 01:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/62/
Title: Masters and Honors thesis talks (https://stable.researchseminars.org/talk/StatisticsandStochastic/62/)
by Masters and Honors students (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Presentation Schedule:

11:00 Krishna Suppiah. Study of Gegenbauer Time Series and Their Statistics

11:15 David Teakle. Generalised adaptive signed correlation index

11:40 Aarjav Khara. Correcting Predictions for Anomalies: Application to Property Prices

12:05 Helen Arnold. Prediction intervals in meta-analysis

Adam Bilchouris (La Trobe University)
2025-08-14 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/63/
Title: Investigating Complex Spatial and Time Series Data (https://stable.researchseminars.org/talk/StatisticsandStochastic/63/)
by Adam Bilchouris (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Several nonparametric estimators of autocovariance function can be found in the literature with various statistical properties and potential drawbacks. Despite their limitations, these estimators remain widely used in many applications.
An R package, CovEsts, will be introduced. The package provides several nonparametric estimators of the autocovariance function with differing properties, through a unified interface and output. The package also includes estimator transforms, estimator corrections, and several metric functions to compare obtained estimates.

Prof Adam Jakubowski (Uniwersytet Mikołaja Kopernika)
2025-08-21 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/64/
Title: Convergence in law in metric and submetric spaces (https://stable.researchseminars.org/talk/StatisticsandStochastic/64/)
by Prof Adam Jakubowski (Uniwersytet Mikołaja Kopernika) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This event is being organised in collaboration with the PVSeminar.

The purpose of the talk is two-fold.
First we will convince the audience that the notion of convergence in law should not be identified with the weak convergence of probability distributions when we leave the safe area of metric spaces. We shall introduce a topology related to the almost sure Skorokhod’s representation, which nowadays is widely used in existence problems of Stochastic Partial Differential Equations. The advantage of this topology is its flexibility and preservation of standard methods, proper to the metric case, in the large class of so-called submetric spaces.

A topological space is submetric if there exists a continuous metric, defined on it, and generating a metric topology (usually weaker than the original topology). As a well-known example, an infinite dimensional, separable Hilbert space with the weak topology may serve here.

The second part of the talk will be devoted to the presentation of the many properties of submetric spaces, which make them a suitable and intuitive tool in the theory of stochastic processes.

Dr Andreas Søjmark (London School of Economics and Political Science)
2025-09-04 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/65/
Title: Particle systems with moving boundaries governed by singular forward-backward interactions (https://stable.researchseminars.org/talk/StatisticsandStochastic/65/)
by Dr Andreas Søjmark (London School of Economics and Political Science) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This event is being organised in collaboration with the PVSeminar.

The topic of this talk is an interacting system of N particles and N moving boundaries which evolve until a final time T. If the particles hit their respective boundaries before or at this time, then they are absorbed, and it is the possibility of this occurring that determines how the boundaries evolve. Before time T, the boundaries should reflect both the realized impact of past absorptions and the expected impact of possible future absorptions, leading to a forward-backward specification. I will give a precise formulation of this problem and address its well-posedness along with a suitable notion of Markovianity. This will take us to a new and interesting type of free boundary problem given by a ‘cascade’ of PDEs representing different configurations of absorbed particles. The motivation comes from the analysis of contagion in financial networks which I shall briefly discuss. Based on joint work with P Jettkant.

Maha Alghamdi (La Trobe University)
2025-10-10 06:00 - 07:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/66/
Title: Multiscaling asymptotic behavior of solutions to random high-order heat equations (https://stable.researchseminars.org/talk/StatisticsandStochastic/66/)
by Maha Alghamdi (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
This talk focuses on high-order partial differential equations with random initial conditions that exhibit both long-memory and cyclic behavior. The study considers cases where the random initial conditions possess spectral singularities either at zero (corresponding to classical long-range dependence) or at nonzero frequencies (corresponding to cyclic long-range dependence). Using spectral methods and scaling techniques, it is shown that, after suitable rescaling and normalization, the solutions converge to Gaussian random fields. For each class of equations, spectral representations and covariance structures of the limiting fields are presented. In the case of odd-order equations, kernel averaging is applied to obtain nonexplosive and nondegenerate limits. The results demonstrate that the nature of the limiting fields depends on whether the equation is of even or odd order and on the presence or absence of a spectral singularity at zero. Several numerical examples illustrate the theoretical findings.

Udara Kumaranathunga (La Trobe University)
2025-10-01 02:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/67/
Title: Meta analysis of quantiles and functions of quantiles (https://stable.researchseminars.org/talk/StatisticsandStochastic/67/)
by Udara Kumaranathunga (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Meta-analysis of continuous outcomes has traditionally relied on methods that use moment-based statistical measures such as means and standard deviations. However, it is often found that in practical scenarios, outcome measures are often skewed. Diabetes-related biomarkers, such as creatinine, triglycerides and microalbumin are several of such examples, which commonly display underlying skewed distributions. For such skewed outcome measures, we introduce meta-analyses of medians, other quantiles and functions of quantiles to comprehensively synthesis information. We consider the commonly reported scenarios of five number summary (minimum, first quartile, median, third quartile, maximum) together with the sample sizes. Within a novel density-based framework, without making any prior assumptions about the underlying distributions, we use flexible quantile-based distributions with percentile matching to estimate the unknown parameters. Additionally, we extend the quantile estimation method to meta-analyse quantiles. We carry out simulation studies to evaluate the approaches and present results for varying number of studies, study sizes, distributions of the outcome measures, and heterogeneity. Together with the simulation results, we report results from an empirical study of meta-analysis of quantiles and interquartile range widths as an example of meta-analysis of functions of quantiles, using real-world data from the Australian Diabetes, Obesity, and Lifestyle (AusDiab) study. We further present our CRAN-based R package (metaquant) that contains the code necessary for straightforward implementation of the introduced approaches.

Masters students (La Trobe University)
2025-10-30 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/68/
Title: Data Science Master's final theses presentations (https://stable.researchseminars.org/talk/StatisticsandStochastic/68/)
by Masters students (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
12:00 Aarjav Khara. Feature Selection and Scaling Directions for Robust Property Price Prediction

12:25 Helen Arnold. A comparison of heterogeneity estimators in meta-analysis: an online tool for researchers, educators, and students

Krishna Suppiah, David Teakle (La Trobe University)
2025-11-13 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/69/
Title: Honour's and Master's final theses presentations (https://stable.researchseminars.org/talk/StatisticsandStochastic/69/)
by Krishna Suppiah, David Teakle (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
12:30 Krishna Suppiah. Study of Gegenbauer Time Series and their Statistics

12:55 David Teakle. The Generalized Adaptive Signed Correlation Index

Various (Various)
2025-12-18 01:00 - 03:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/70/
Title: Extended La Trobe University Statistics Seminar (https://stable.researchseminars.org/talk/StatisticsandStochastic/70/)
by Various (Various) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Extended La Trobe University Statistics Seminar
Honouring the Retirement of A/Professors Paul Kabaila
Monday 15 December, 12:00, PS2-313

Speakers:

• Professor Alan H. Welsh
• Dr J. H. D. S. P. Tissera
• Professor Chris J. Lloyd
• Dr Waruni Abeysekera
• Dr Rheanna Mainzer
• Dr Khageswor Giri
• Dr Ayesha Perera
• Dr Rupert Kuveke
• Dr Christeen Wijethunga
• Professor Sidney Morris

Serhii Kravchenko (La Trobe University)
2025-12-15 05:00 - 06:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/71/
Title: Inference for Stochastic Processes from Discretely Sampled Data (https://stable.researchseminars.org/talk/StatisticsandStochastic/71/)
by Serhii Kravchenko (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
PhD progress talk.

PhD students (La Trobe University)
2025-11-27 01:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/72/
Title: PhD students' preconference talks (https://stable.researchseminars.org/talk/StatisticsandStochastic/72/)
by PhD students (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
PhD students' preconference talks for AustMS 2025 and ASC 2025.

Nemini Samarakoon (La Trobe University)
2025-12-18 07:00 - 08:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/73/
Title: Construction, Properties and Simulation of Haar-Based Multifractional Processes with Rmfrac R package (https://stable.researchseminars.org/talk/StatisticsandStochastic/73/)
by Nemini Samarakoon (La Trobe University) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
Multifractional processes are non-stationary stochastic processes that allow modeling the time-dependent regularity in their trajectories. This talk introduces a new class of multifractional processes, Gaussian Haar-based multifractional processes (GHBMP) based on their Haar wavelet series representation. GHBMP provides computationally efficient simulation algorithms and can capture abrupt changes in the roughness of trajectories. The theoretical properties of the GHBMP will also be discussed. Additionally, Rmfrac R package will be introduced. Rmfrac facilitates the simulation and analyses of multifractional processes.

Prof O.Aryasova (Friedrich Schiller University, Jena, Germany)
2026-03-19 01:00 - 02:00 | https://stable.researchseminars.org/talk/StatisticsandStochastic/74/
Title: The hard membrane process and transport barriers of turbulent flows (https://stable.researchseminars.org/talk/StatisticsandStochastic/74/)
by Prof O.Aryasova (Friedrich Schiller University, Jena, Germany) as part of La Trobe University Statistics and Stochastic zoom seminar

Abstract
In confined fusion plasma devices like tokamaks, turbulence is always present to some degree and it has the bad effect of dispersing heat and particles from the central very hot and dense region to the boundary and then to the surrounding areas and walls. Sometimes barriers arise in intermediate regions, which reduce this dispersion. These barriers, also called zonal flows, are thin layers of plasma where the fluid velocity is not turbulent as everywhere else but ordered, roughly laminar, directed perpendicularly to the direction of dispersion.

There is still active research to understand how these barriers arise, whether they can be triggered, how they evolve, the precise links with other plasma dynamical features and parameters. Our aim here is to rigorously define a mathematical model of a sharp heat-diffusion barrier. In a scaling limit of theturbulence model with separation of scales we get a heat equation with space-dependent diffusion coefficient, poorly diffusing near the barrier; then we investigate the scaling limit when the diffused barrier converges to a sharp separating surface and describe the limit by means of the stochastic process called Brownian motion with a hard membrane.
