BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Prof. Dr. Igor Mandel (NJ\, USA) & Prof.Dr. Stan Lipovetsky (MN\, USA) (Retired) DTSTART:20260402T040000Z DTEND:20260402T050000Z DTSTAMP:20260530T184753Z UID:AMIS/1 DESCRIPTION:Title: Simulation-Based Insights and Novel Criteria for Linear Regression Mod eling\nby Prof. Dr. Igor Mandel (NJ\, USA) & Prof.Dr. Stan Lipovetsky (MN\, USA) (Retired) as part of Asymptotic Methods in Statistics\n\nAbstra ct: TBA\n\nAbstract: We study asymptotic behavior of the averaged integral s of a Lévy-driven\nlinear process weighted by a complex exponent of poly nomials with real coefficients.\nSuch functionals naturally arise in the p roblems relating to nonlinear regression\nanalysis and signal processing\, specifically in the estimation of parameters of\nfrequency-modulated sign als.\n Under some conditions on the Lévy process and kernel defining th e linear process\,\nwe get a uniform strong law of large numbers for this weighted process. More\nprecisely\, it is shown that the considered integr als converge a.s. to zero uniformly\nover all the values of the real coeff icients of the polynomials of fixed order.\n The result obtained is then used to prove strong consistency of LSE for the\nparameters of linearly-m odulated trigonometric signal (chirp signal) observed against\nthe backgro und of shot noise described above.\n LOCATION:https://stable.researchseminars.org/talk/AMIS/1/ END:VEVENT BEGIN:VEVENT SUMMARY:PhD student Viktor Hladun (National Technical University of Ukrain e “Igor Sikorsky Kyiv Polytechnic Institute”) (National Technical Univ ersity of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”) DTSTART:20260408T140000Z DTEND:20260408T150000Z DTSTAMP:20260530T184753Z UID:AMIS/2 DESCRIPTION:Title: On uniform Strong Law of Large Numbers for weighted shot noise and con sistency of the Least Squares Estimator of chirp signal parameters\nby PhD student Viktor Hladun (National Technical University of Ukraine “Ig or Sikorsky Kyiv Polytechnic Institute”) (National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”) as part of Asym ptotic Methods in Statistics\n\n\nAbstract\nWe study asymptotic behavior o f the averaged integrals of a Lévy-driven\nlinear process weighted by a c omplex exponent of polynomials with real coefficients.\nSuch functionals n aturally arise in the problems relating to nonlinear regression\nanalysis and signal processing\, specifically in the estimation of parameters of\nf requency-modulated signals.\n Under some conditions on the Lévy process and kernel defining the linear process\,\nwe get a uniform strong law of large numbers for this weighted process. More\nprecisely\, it is shown tha t the considered integrals converge a.s. to zero uniformly\nover all the v alues of the real coefficients of the polynomials of fixed order.\n The result obtained is then used to prove strong consistency of LSE for the\np arameters of linearly-modulated trigonometric signal (chirp signal) observ ed against\nthe background of shot noise described above.\n\nThe results a re joint with Prof. Dr. Alexander Ivanov (National Technical University of Ukraine\n“Igor Sikorsky Kyiv Polytechnic Institute”).\n LOCATION:https://stable.researchseminars.org/talk/AMIS/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Doctor of Phys. and Math. Sc.\, Leading Researcher Sergiy Shklyar (Institute of Geological Sciences NAS of Ukraine) DTSTART:20260415T140000Z DTEND:20260415T150000Z DTSTAMP:20260530T184753Z UID:AMIS/3 DESCRIPTION:Title: Multiframe resolution enhancement in a frequency domain\nby Doctor of Phys. and Math. Sc.\, Leading Researcher Sergiy Shklyar (Institute of Geological Sciences NAS of Ukraine) as part of Asymptotic Methods in Stati stics\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AMIS/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Prof. Dr. Baran Sandor (University of Debrecen\, Hungary) DTSTART:20260422T140000Z DTEND:20260422T150000Z DTSTAMP:20260530T184753Z UID:AMIS/4 DESCRIPTION:Title: Fair Scores for Multivariate Gaussian Forecasts\nby Prof. Dr. Bara n Sandor (University of Debrecen\, Hungary) as part of Asymptotic Methods in Statistics\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/AMIS/4/ END:VEVENT BEGIN:VEVENT SUMMARY:Ptof. Dr. Volodymyr V. Anisimov (Data Science Director\, Data Scie nce\, Center for Design & Analysis\, Amgen\, London\, UK) DTSTART:20260506T140000Z DTEND:20260506T150000Z DTSTAMP:20260530T184753Z UID:AMIS/5 DESCRIPTION:Title: Advanced Data-Driven Statistical Technologies for Designing and Foreca sting Operation in Late-stage Clinical Trials\nby Ptof. Dr. Volodymyr V. Anisimov (Data Science Director\, Data Science\, Center for Design & An alysis\, Amgen\, London\, UK) as part of Asymptotic Methods in Statistics\ n\n\nAbstract\nAbstract: Clinical trials in the modern era are characteriz ed by their complexity and very high costs. With the need to recruit hundr eds or even thousands of patients across multiple clinical sites and count ries\, conducting efficient and effective trials has become a major challe nge.\nDesigning and forecasting clinical trial operations remains one of t he most pressing challenges in modern drug development\, with inefficient patient enrolment being a leading contributor to costly delays. \nThis tal k presents recent advances in analytic and statistical methodologies aimed at improving the predictability and efficiency of clinical trial operatio n.\nWe introduce innovative data-driven technologies that are based on a r igorous and practical statistical framework (hierarchic stochastic models with random parameters) and enhance recruitment forecasting by accounting for key sources of uncertainty\, including variability in site activation timelines\, heterogeneous enrolment rates across sites\, and temporal stoc hasticity. These models enable dynamic\, stage-specific projections that b etter align operational plans with real-world trial behavior.\nA framework for optimizing cost-efficient recruitment strategies through intelligent site and country selection is also presented. This methodology incorporate s operational constraints such as regional enrolment caps and costs to bal ance feasibility and resource allocation.\nInterim reforecasting approache s that leverage accumulating data to adaptively adjust recruitment plans a re discussed with the goal of achieving the probability of meeting enrolme nt milestones. Additionally\, statistical techniques for centralized monit oring are introduced to identify atypical performance patterns\, flagging under- or over-performing sites and informing operational interventions.\n The talk also covers methods for forecasting key operational metrics criti cal to trial planning and oversight—such as projecting event accrual in oncology trials. \nThe utility of these approaches is demonstrated using v arious case studies that illustrate their application in complex\, global clinical programs and show how these advanced tools are reshaping clinical trial operations\, cost management\, and ultimately improved outcomes.Col lectively\, these innovations can significantly improve trial predictabili ty and efficiency and accelerate the drug development process.\nOur resear ch work "Forecasting and cost-efficient designing restricted enrolment in clinical trials" was recognized by the 2025 Award for Statistical Excellen ce in the Pharmaceutical Industry from the Royal Statistical Society and P SI (UK).\n LOCATION:https://stable.researchseminars.org/talk/AMIS/5/ END:VEVENT BEGIN:VEVENT SUMMARY:PhD student Daniel Gorbunov (Taras Shevchenko National University of Kyiv) DTSTART:20260513T140000Z DTEND:20260513T150000Z DTSTAMP:20260530T184753Z UID:AMIS/6 DESCRIPTION:Title: Nonparametric regression estimators for mixtures with varying concentr ations\nby PhD student Daniel Gorbunov (Taras Shevchenko National Univ ersity of Kyiv) as part of Asymptotic Methods in Statistics\n\n\nAbstract\ nFinite mixture models naturally arise in statistical analysis of biologic al and sociological data. If the sub-population which a subject belongs to is not known exactly\, the distribution of its variables is a mixture of the sub-populations’ distributions. In the classical finite mixture mode ls (FMM) the concentrations of the components in the mixture (mixing proba bilities) are the same for all observations. In a more flexible mixture wi th varying concentrations model (MVC)\, the concentrations are different f or different observations.\n\nRegression models are typically applied to d escribe dependency between different numerical variables of one subject. I n the case of homogeneous sample there exist many non-parametric estimator s of the regression function\, such as the Nadaraya-Watson estimator (NWE) and local linear regression estimator (LLRE). For homogeneous samples\, N WE demonstrates an inappropriate bias in points where the regressor probab ility density function (PDF) has discontinuity (jump points). For such a s cenario\, the LLRE stands as a remedy\, having a significantly smaller bia s.\n\nIn this talk\, we consider a modification of NWE (mNWE) and LLRE (mL LRE) for the estimation of the regression function of some MVC component. We will show that under suitable assumptions\, the modified estimators are asymptotically normal. Moreover\, the rate of convergence for the mNWE is different at different points of continuity and discontinuity of the regr essor's PDF respectively\, whereas the mLLRE preserves the same rate of co nvergence for both cases.\n LOCATION:https://stable.researchseminars.org/talk/AMIS/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Dr. Yurii Malitskyi (Assistant Professor) (Faculty of Mathematics\ , University of Vienna\, Austria) DTSTART:20260520T140000Z DTEND:20260520T150000Z DTSTAMP:20260530T184753Z UID:AMIS/7 DESCRIPTION:Title: Implicit bias in optimization algorithms\nby Dr. Yurii Malitskyi ( Assistant Professor) (Faculty of Mathematics\, University of Vienna\, Aust ria) as part of Asymptotic Methods in Statistics\n\n\nAbstract\nIn this ta lk\, we will explore the role of implicit bias in optimization algorithms. Implicit bias refers to the tendency of an algorithm to converge to a spe cific solution even in the absence of an explicit regularization term in i ts formulation. In other words\, implicit bias emerges from how the algori thm itself interacts with the objective function it’s trying to minimize . We will demonstrate this concept through a few surprising applications.\ n\nAbstract. In this talk\, we will explore the role of implicit bias in optimization algorithms. Implicit bias refers to the tendency of an algori thm to converge to a specific solution even in the absence of an explicit regularization term in its formulation. In other words\, implicit bias eme rges from how the algorithm itself interacts with the objective function i t’s trying to minimize. We will demonstrate this concept through a few s urprising applications.\n LOCATION:https://stable.researchseminars.org/talk/AMIS/7/ END:VEVENT BEGIN:VEVENT SUMMARY:Prof. Dr. Iryna Rozora (Prof. Dr. Iryna Rozora (Head of Department of Applied Statistics\, Faculty of Cybernetics and Computer Sciences\, Ta ras Shevchenko National University of Kyiv) DTSTART:20260527T140000Z DTEND:20260527T150000Z DTSTAMP:20260530T184753Z UID:AMIS/8 DESCRIPTION:Title: On the Statistical Properties of Impulse Response Function Estimators< /a>\nby Prof. Dr. Iryna Rozora (Prof. Dr. Iryna Rozora (Head of Department of Applied Statistics\, Faculty of Cybernetics and Computer Sciences\, Ta ras Shevchenko National University of Kyiv) as part of Asymptotic Methods in Statistics\n\n\nAbstract\nEstimating stochastic linear systems using Im pulse Response Functions (IRFs) is a fundamental problem with broad releva nce across multiple disciplines. IRFs describe how a system responds to ex ternal inputs\, providing essential insights into system behavior and cont rol. Such analyses have important applications in signal processing\, cont rol theory\, econometrics\, oceanography\, and related fields.\nIn this ta lk\, I will focus on the estimation of IRFs for Single-Input Single-Output (SISO) systems. We consider a time-invariant continuous linear system dri ven by zero-mean stationary Gaussian input processes. The aim of the study is to develop a method for estimating the unknown IRF based on observatio ns of both the system’s input and output signals. Our approach is based on a cross-correlogram estimator\, which computes the correlation between the input and output processes. We establish conditions for asymptotic unb iasedness and consistency\, analyze the rate of convergence\, and propose a statistical goodness-of-fit test for validating the estimated IRF. The t heoretical results are supported by numerical simulations.\n LOCATION:https://stable.researchseminars.org/talk/AMIS/8/ END:VEVENT BEGIN:VEVENT SUMMARY:Dr. Andriy Malenko (senior researcher of V. M. Glushkov Institute of Cybernetics NAS of Ukraine) DTSTART:20260603T140000Z DTEND:20260603T150000Z DTSTAMP:20260530T184753Z UID:AMIS/9 DESCRIPTION:Title: Adaptive radar system configuration tuning\nby Dr. Andriy Malenko (senior researcher of V. M. Glushkov Institute of Cybernetics NAS of Ukrai ne) as part of Asymptotic Methods in Statistics\n\nInteractive livestream: https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE3QT09 Passcode (if necessary) 785163\n\nAbstract\nThe talk focuses on an algor ithm for parameters tuning for radar-based sensing systems for motion moni toring. The algorithm adapts parameters for better system performance acco rding to the estimated motion zone properties\, i.e. mean and maximum dist ance to objects and their velocities. A new approach for determination of radar height position and tilt angle is proposed and experimentally tested . The method uses radar measurement points cloud only and may be applied f or optimization of distance and velocity ranges and reduction of artifacts . Error of tilt angle estimation is 0.7-1.6 degrees in range of 0-45 degre es. Height estimation error depends on moving object height and is less th an 1 m. The main goal of algorithm is estimation of motion zone and corres ponding adaptive change of radar-system configuration parameters for optim izing system's field of view and performance. The proposed approach may be used for indoor and outdoor motion detection and activity classification in applications such as health diagnostics\, health monitoring\, surveilla nce\, occupancy sensing\, and automotive.\n LOCATION:https://stable.researchseminars.org/talk/AMIS/9/ URL:https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE3Q T09 Passcode (if necessary) 785163 END:VEVENT END:VCALENDAR