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:20260510T120530Z 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:20260510T120530Z 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:20260510T120530Z 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:20260510T120530Z 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:20260510T120530Z 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:20260510T120530Z 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\nInteractive livestream: https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhD UFNGTVE3QT09 Passcode (if necessary) 785163\n\nAbstract\nFinite mixture m odels naturally arise in statistical analysis of biological and sociologic al data. If the sub-population which a subject belongs to is not known exa ctly\, the distribution of its variables is a mixture of the sub-populatio ns’ distributions. In the classical finite mixture models (FMM) the conc entrations of the components in the mixture (mixing probabilities) are the same for all observations. In a more flexible mixture with varying concen trations model (MVC)\, the concentrations are different for different obse rvations.\n\nRegression models are typically applied to describe dependenc y between different numerical variables of one subject. In the case of hom ogeneous sample there exist many non-parametric estimators of the regressi on function\, such as the Nadaraya-Watson estimator (NWE) and local linear regression estimator (LLRE). For homogeneous samples\, NWE demonstrates a n inappropriate bias in points where the regressor probability density fun ction (PDF) has discontinuity (jump points). For such a scenario\, the LLR E stands as a remedy\, having a significantly smaller bias.\n\nIn this tal k\, we consider a modification of NWE (mNWE) and LLRE (mLLRE) for the esti mation of the regression function of some MVC component. We will show that under suitable assumptions\, the modified estimators are asymptotically n ormal. Moreover\, the rate of convergence for the mNWE is different at dif ferent points of continuity and discontinuity of the regressor's PDF respe ctively\, whereas the mLLRE preserves the same rate of convergence for bot h cases.\n LOCATION:https://stable.researchseminars.org/talk/AMIS/6/ URL:https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE3Q T09 Passcode (if necessary) 785163 END:VEVENT END:VCALENDAR