BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Cláudia Nunes (CEMAT and IST) DTSTART:20200528T100000Z DTEND:20200528T110000Z DTSTAMP:20260404T094318Z UID:ProbStat/1 DESCRIPTION:Title: Quasi-analytical solution of an investment problem with decreasing investment cost due to technological innovations\nby Cláudia Nunes ( CEMAT and IST) as part of Probability & Statistics (IST-CEMAT\, FC-CEAUL\ , ULisbon)\n\n\nAbstract\nIn this talk we address\, in the context of real options\, an investment problem with two sources of uncertainty: the pric e (reflected in the revenue of the firm) and the level of technology. The level of technology impacts in the investment cost\, that decreases when t here is a technology innovation. The price follows a geometric Brownian mo tion\, whereas the technology innovations are driven by a Poisson process. As a consequence\, the investment region may be attained in a continuous way (due to an increase of the price) or in a discontinuous way (due to a sudden decrease of the investment cost).\n\nFor this optimal stopping prob lem no analytical solution is known\, and therefore we propose a quasi-ana lytical method to find an approximated solution that preserves the qualita tive features of the exact solution. This method is based on a truncation procedure and we prove that the truncated solution converges to the soluti on of the original problem.\n\nWe provide results for the comparative stat ics for the investment thresholds. These results show interesting behavior s\, particularly\, the investment may be postponed or anticipated with the intensity of the technology innovations and with their impact on the inve stment cost.\n\n(joint work with Carlos Oliveira and Rita Pimentel)\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Igor Kravchenko (CEMAT and IST) DTSTART:20200618T100000Z DTEND:20200618T110000Z DTSTAMP:20260404T094318Z UID:ProbStat/2 DESCRIPTION:Title: Investment problem with switching modes\nby Igor Kravchenko (C EMAT and IST) as part of Probability & Statistics (IST-CEMAT\, FC-CEAUL\, ULisbon)\n\n\nAbstract\nIn this talk we will look at the optimal control problem of a firm that may operate in two different modes\, one being more risky than the other\, in the sense that in case the demand decreases\, t he return of the risky mode is lower than with the more conservative mode. On the other side\, in case the demand increases\, the opposite holds. Th e switches between these two alternative modes have associated costs. In b oth modes\, there is the option to exit the market.\nWe will focus on two different parameter scenarios\, that describe particular (and somehow extr eme) economic situations. In the first scenario\, we assume that the marke t is expected to increase in such a way that once the firm is producing in the more risky mode\, it is never optimal to switch to the more conservat ive one. In the second scenario\, there is a hysteresis region\, where the firm is waiting in the more risky mode\, in production\, until some drop or increase in the demand leads to an exit or changing to the more conserv ative mode. This hysteresis region cannot be attained under continuous pro duction.\nWe then address the problem of the optimal time to invest under each situation. Depending on the relation between the switching costs (equ al or different from one mode to another)\, it may happen that the firm in vests in the hysteresis region.
\nJoint work with Cláudia Nunes and C arlos Oliveira\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Maria do Rosário Oliveira (CEMAT and IST) DTSTART:20200625T100000Z DTEND:20200625T110000Z DTSTAMP:20260404T094318Z UID:ProbStat/3 DESCRIPTION:Title: Theoretical foundations of forward feature selection methods based on mutual information\nby Maria do Rosário Oliveira (CEMAT and IST) as part of Probability & Statistics (IST-CEMAT\, FC-CEAUL\, ULisbon)\n\n \nAbstract\nFeature selection problems arise in a variety of applications\ , such as microarray analysis\, clinical prediction\, text categorization\ , image classification and face recognition\, multi-label learning\, and c lassification of internet traffic. Among the various classes of methods\, forward feature selection methods based on mutual information have become very popular and are widely used in practice. However\, comparative evalua tions of these methods have been limited by being based on specific datase ts and classifiers. In this talk\, we discuss a theoretical framework that allows evaluating the methods based on their theoretical properties. The estimation difficulties of the method’s objective functions will also be addressed.\n\nThis is a joint work with Francisco Macedo\, António Pache co\, and Rui Valadas.\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Joaquim Ferreira (Laboratório de Farmacologia Clínica e Terapêu tica\, Faculdade de Medicina\, Universidade de Lisboa) DTSTART:20200723T100000Z DTEND:20200723T110000Z DTSTAMP:20260404T094318Z UID:ProbStat/4 DESCRIPTION:Title: COVID\, uncertainty and clinical trials\nby Joaquim Ferreira ( Laboratório de Farmacologia Clínica e Terapêutica\, Faculdade de Medici na\, Universidade de Lisboa) as part of Probability & Statistics (IST-CEM AT\, FC-CEAUL\, ULisbon)\n\n\nAbstract\nThe current COVID-19 pandemic is p utting an enormous pressure not just in the society but also in all the sc ientific community.\n\nIf we want to follow a scientific approach to respo nd to the doubts and challenges that were generated\, we need to find a ba lance between the most robust data\, the best experimental methodologies t o address the new problems and all the uncertainty associated.\n\nIn this presentation we will try to address this balance between best available da ta\, clinical research methodology and uncertainty applied to what we know about pandemics\, vaccine development and clinical trials. There will be a particular focus on the COVID-19 pandemic data and current research effo rts for the development of vaccines and efficacious treatments.\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/4/ END:VEVENT BEGIN:VEVENT SUMMARY:Miguel de Carvalho (University of Edinburgh) DTSTART:20200716T100000Z DTEND:20200716T110000Z DTSTAMP:20260404T094318Z UID:ProbStat/5 DESCRIPTION:Title: Elements of Bayesian geometry\nby Miguel de Carvalho (Universi ty of Edinburgh) as part of Probability & Statistics (IST-CEMAT\, FC-CEAU L\, ULisbon)\n\n\nAbstract\nIn this talk\, I will discuss a geometric inte rpretation to Bayesian inference that will yield a natural measure of the level of agreement between priors\, likelihoods\, and posteriors. The star ting point for the construction of the proposed geometry is the observatio n that the marginal likelihood can be regarded as an inner product between the prior and the likelihood. A key concept in our geometry is that of co mpatibility\, a measure which is based on the same construction principles as Pearson correlation\, but which can be used to assess how much the pri or agrees with the likelihood\, to gauge the sensitivity of the posterior to the prior\, and to quantify the coherency of the opinions of two expert s. Estimators for all the quantities involved in our geometric setup are d iscussed\, which can be directly computed from the posterior simulation ou tput. Some examples are used to illustrate our methods\, including data re lated to on-the-job drug usage\, midge wing length\, and prostate cancer.\ n\nJoint work with G. L. Page and with B. J. Barney\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Manuel Cabral Morais (CEMAT and IST) DTSTART:20200514T100000Z DTEND:20200514T110000Z DTSTAMP:20260404T094318Z UID:ProbStat/6 DESCRIPTION:Title: On ARL-unbiased charts to monitor the traffic intensity of a singl e server queue\nby Manuel Cabral Morais (CEMAT and IST) as part of Pro bability & Statistics (IST-CEMAT\, FC-CEAUL\, ULisbon)\n\n\nAbstract\n

We know too well that the eff ective operation of a queueing system requires maintaining the traffic int ensity at a target value. This important measure of congestion can be moni tored by using control charts\, such as the one found in the seminal work by Bhat and Rao (1972) or more recently in Chen and Zhou (2015). For all i ntents and purposes\, this paper focus on three control statistics chosen by Morais and Pacheco (2016) for their simplicity\, recursive and Markovia n character:

Since an upward an d a downward shift in the traffic intensity are associated with a deterior ation and an improvement (respectively) of the quality of service\, the ti mely detection of these changes is an imperative requirement\, hence\, beg ging for the use of ARL-unbiased charts Pignatiello et al. (1995)\, in the sense that they detect any shifts in the traffic intensity sooner than th ey trigger a false alarm. In this paper\, we focus on the design of these type of charts for the traffic intensity of the three single server queues mentioned above.

Joint work with Sven Knoth< /p>\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Ismael Lemhadri (Stanford University) DTSTART:20200709T150000Z DTEND:20200709T160000Z DTSTAMP:20260404T094318Z UID:ProbStat/7 DESCRIPTION:Title: LassoNet: A Neural Network with Feature Sparsity\nby Ismael Le mhadri (Stanford University) as part of Probability & Statistics (IST-CEM AT\, FC-CEAUL\, ULisbon)\n\n\nAbstract\nMuch work has been done recently t o make neural networks more interpretable\, and one obvious approach is to arrange for the network to use only a subset of the available features. I n linear models\, Lasso (or $\\ell_1$-regularized) regression assigns zero weights to the most irrelevant or redundant features\, and is widely used in data science. However the Lasso only applies to linear models. Here we introduce LassoNet\, a neural network framework with global feature selec tion. Our approach enforces a hierarchy: specifically a feature can partic ipate in a hidden unit only if its linear representative is active. Unlike other approaches to feature selection for neural nets\, our method uses a modified objective function with constraints\, and so integrates feature selection with the parameter learning directly. As a result\, it delivers an entire regularization path of solutions with a range of feature sparsit y. On systematic experiments\, LassoNet significantly outperforms state-of -the-art methods for feature selection and regression. The LassoNet method uses projected proximal gradient descent\, and generalizes directly to de ep networks. It can be implemented by adding just a few lines of code to a standard neural network.\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/7/ END:VEVENT BEGIN:VEVENT SUMMARY:Conceição Amado (Instituto Superior Técnico and CEMAT) DTSTART:20201001T120000Z DTEND:20201001T130000Z DTSTAMP:20260404T094318Z UID:ProbStat/8 DESCRIPTION:Title: From high dimensional space to a random low dimensional space\ nby Conceição Amado (Instituto Superior Técnico and CEMAT) as part of P robability & Statistics (IST-CEMAT\, FC-CEAUL\, ULisbon)\n\n\nAbstract\nW hat might happen if we have points in a high dimensional space and one dec ided to project them into a random low dimensional space?\n\nIn this semin ar\, we will discuss this subject and will see some simple applications.\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/8/ END:VEVENT BEGIN:VEVENT SUMMARY:Boris Beranger (School of Mathematics and Statistics\, University New South Wales\, Sydney) DTSTART:20201015T100000Z DTEND:20201015T110000Z DTSTAMP:20260404T094318Z UID:ProbStat/10 DESCRIPTION:Title: High-dimensional inference for max-stable processes\nby Boris Beranger (School of Mathematics and Statistics\, University New South Wal es\, Sydney) as part of Probability & Statistics (IST-CEMAT\, FC-CEAUL\, ULisbon)\n\n\nAbstract\nDroughts\, high temperatures and strong winds are key causes of the recent bushfires that have touched a major part of the A ustralian territory. Such extreme events seem to appear with increasing fr equency\, creating an urgent need to better understand the behaviour of ex treme environmental phenomena. Max-stable processes are a widely popular t ool to model spatial extreme events with several flexible models available in the literature. For inference on max-stable models\, exact likelihood estimation becomes quickly computationally intractable as the number of sp atial locations grows\, limiting their applicability to large study region s or fine grids. In this talk\, we introduce two methodologies based on co mposite likelihoods\, to circumvent this issue. First\, we assume the occu rrence times of maxima available in order to incorporate the Stephenson-Ta wn concept into the composite likelihood framework. Second\, we propose to aggregate the information between locations into histograms and to derive a composite likelihood variation for these summaries. The significant imp rovements in performance of each estimation procedures is established thro ugh simulation studies and illustrated on two temperature datasets from Au stralia.\n\nJoint seminar CEMAT and CEAUL\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/10/ END:VEVENT BEGIN:VEVENT SUMMARY:Carina Silva (Escola Superior de Tecnologia da Saúde from Lisbon and CEAUL) DTSTART:20201022T120000Z DTEND:20201022T130000Z DTSTAMP:20260404T094318Z UID:ProbStat/11 DESCRIPTION:Title: Impact of OVL Variation on AUC Bias Estimated by Non-parametric M ethods\nby Carina Silva (Escola Superior de Tecnologia da Saúde from Lisbon and CEAUL) as part of Probability & Statistics (IST-CEMAT\, FC-C EAUL\, ULisbon)\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/11/ END:VEVENT BEGIN:VEVENT SUMMARY:Jorge Milhazes de Freitas (Faculty of Sciences of the University of Porto and CMUP) DTSTART:20201028T130000Z DTEND:20201028T140000Z DTSTAMP:20260404T094318Z UID:ProbStat/12 DESCRIPTION:Title: Enriched functional limit theorems for dynamical systems\nby Jorge Milhazes de Freitas (Faculty of Sciences of the University of Porto and CMUP) as part of Probability & Statistics (IST-CEMAT\, FC-CEAUL\, UL isbon)\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/12/ END:VEVENT BEGIN:VEVENT SUMMARY:Ana Paula Martins (Faculty of Sciences of University of Beira Inte rior and CMA) DTSTART:20201112T130000Z DTEND:20201112T140000Z DTSTAMP:20260404T094318Z UID:ProbStat/13 DESCRIPTION:Title: Extremes modelling of imputed missing data under a periodic contr ol\nby Ana Paula Martins (Faculty of Sciences of University of Beira I nterior and CMA) as part of Probability & Statistics (IST-CEMAT\, FC-CEAU L\, ULisbon)\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/ProbStat/13/ END:VEVENT END:VCALENDAR