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:20260512T102635Z UID:AMIS/1 DESCRIPTION:Title: Sim ulation-Based Insights and Novel Criteria for Linear Regression Modeling\nby Prof. Dr. Igor Mandel (NJ\, USA) & Prof.Dr. Stan Lipovetsky (MN\, U SA) (Retired) as part of Asymptotic Methods in Statistics\n\nAbstract: TBA \n\nAbstract: We study asymptotic behavior of the averaged integrals of a Lévy-driven\nlinear process weighted by a complex exponent of polynomials with real coefficients.\nSuch functionals naturally arise in the problems relating to nonlinear regression\nanalysis and signal processing\, specif ically in the estimation of parameters of\nfrequency-modulated signals.\n Under some conditions on the Lévy process and kernel defining the linea r process\,\nwe get a uniform strong law of large numbers for this weighte d process. More\nprecisely\, it is shown that the considered integrals con verge a.s. to zero uniformly\nover all the values of the real coefficients of the polynomials of fixed order.\n The result obtained is then used t o prove strong consistency of LSE for the\nparameters of linearly-modulate d trigonometric signal (chirp signal) observed against\nthe background of shot noise described above.\n LOCATION:https://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:20260512T102635Z UID:AMIS/2 DESCRIPTION:Title: On uniform Strong Law of Large Numbers for weighted shot noise and consistenc y of the Least Squares Estimator of chirp signal parameters\nby PhD st udent Viktor Hladun (National Technical University of Ukraine “Igor Siko rsky Kyiv Polytechnic Institute”) (National Technical University of Ukra ine “Igor Sikorsky Kyiv Polytechnic Institute”) as part of Asymptotic Methods in Statistics\n\n\nAbstract\nWe study asymptotic behavior of the a veraged integrals of a Lévy-driven\nlinear process weighted by a complex exponent of polynomials with real coefficients.\nSuch functionals naturall y arise in the problems relating to nonlinear regression\nanalysis and sig nal processing\, specifically in the estimation of parameters of\nfrequenc y-modulated signals.\n Under some conditions on the Lévy process and ke rnel defining the linear process\,\nwe get a uniform strong law of large n umbers for this weighted process. More\nprecisely\, it is shown that the c onsidered integrals converge a.s. to zero uniformly\nover all the values o f the real coefficients of the polynomials of fixed order.\n The result obtained is then used to prove strong consistency of LSE for the\nparamete rs of linearly-modulated trigonometric signal (chirp signal) observed agai nst\nthe background of shot noise described above.\n\nThe results are join t with Prof. Dr. Alexander Ivanov (National Technical University of Ukrain e\n“Igor Sikorsky Kyiv Polytechnic Institute”).\n LOCATION:https://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:20260512T102635Z UID:AMIS/3 DESCRIPTION:Title: Mul tiframe resolution enhancement in a frequency domain\nby Doctor of Phy s. and Math. Sc.\, Leading Researcher Sergiy Shklyar (Institute of Geologi cal Sciences NAS of Ukraine) as part of Asymptotic Methods in Statistics\n \nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/AMIS/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Prof. Dr. Baran Sandor (University of Debrecen\, Hungary) DTSTART:20260422T140000Z DTEND:20260422T150000Z DTSTAMP:20260512T102635Z UID:AMIS/4 DESCRIPTION:Title: Fai r Scores for Multivariate Gaussian Forecasts\nby Prof. Dr. Baran Sando r (University of Debrecen\, Hungary) as part of Asymptotic Methods in Stat istics\n\nAbstract: TBA\n LOCATION:https://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:20260512T102635Z UID:AMIS/5 DESCRIPTION:Title: Adv anced Data-Driven Statistical Technologies for Designing and Forecasting O peration in Late-stage Clinical Trials\nby Ptof. Dr. Volodymyr V. Anis imov (Data Science Director\, Data Science\, Center for Design & Analysis\ , Amgen\, London\, UK) as part of Asymptotic Methods in Statistics\n\n\nAb stract\nAbstract: Clinical trials in the modern era are characterized by t heir complexity and very high costs. With the need to recruit hundreds or even thousands of patients across multiple clinical sites and countries\, conducting efficient and effective trials has become a major challenge.\nD esigning and forecasting clinical trial operations remains one of the most pressing challenges in modern drug development\, with inefficient patient enrolment being a leading contributor to costly delays. \nThis talk prese nts recent advances in analytic and statistical methodologies aimed at imp roving the predictability and efficiency of clinical trial operation.\nWe introduce innovative data-driven technologies that are based on a rigorous and practical statistical framework (hierarchic stochastic models with ra ndom parameters) and enhance recruitment forecasting by accounting for key sources of uncertainty\, including variability in site activation timelin es\, heterogeneous enrolment rates across sites\, and temporal stochastici ty. These models enable dynamic\, stage-specific projections that better a lign operational plans with real-world trial behavior.\nA framework for op timizing cost-efficient recruitment strategies through intelligent site an d country selection is also presented. This methodology incorporates opera tional constraints such as regional enrolment caps and costs to balance fe asibility and resource allocation.\nInterim reforecasting approaches that leverage accumulating data to adaptively adjust recruitment plans are disc ussed with the goal of achieving the probability of meeting enrolment mile stones. Additionally\, statistical techniques for centralized monitoring a re introduced to identify atypical performance patterns\, flagging under- or over-performing sites and informing operational interventions.\nThe tal k also covers methods for forecasting key operational metrics critical to trial planning and oversight—such as projecting event accrual in oncolog y trials. \nThe utility of these approaches is demonstrated using various case studies that illustrate their application in complex\, global clinica l programs and show how these advanced tools are reshaping clinical trial operations\, cost management\, and ultimately improved outcomes.Collective ly\, these innovations can significantly improve trial predictability and efficiency and accelerate the drug development process.\nOur research work "Forecasting and cost-efficient designing restricted enrolment in clinica l trials" was recognized by the 2025 Award for Statistical Excellence in t he Pharmaceutical Industry from the Royal Statistical Society and PSI (UK) .\n LOCATION:https://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:20260512T102635Z UID:AMIS/6 DESCRIPTION:Title: Non parametric regression estimators for mixtures with varying concentrations< /a>\nby PhD student Daniel Gorbunov (Taras Shevchenko National University of Kyiv) as part of Asymptotic Methods in Statistics\n\nInteractive livest ream: https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE 3QT09 Passcode (if necessary) 785163\n\nAbstract\nFinite mixture models n aturally arise in statistical analysis of biological 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’ d istributions. In the classical finite mixture models (FMM) the concentrati ons of the components in the mixture (mixing probabilities) are the same f or all observations. In a more flexible mixture with varying concentration s model (MVC)\, the concentrations are different for different observation s.\n\nRegression models are typically applied to describe dependency betwe en different numerical variables of one subject. In the case of homogeneou s sample there exist many non-parametric estimators of the regression func tion\, such as the Nadaraya-Watson estimator (NWE) and local linear regres sion estimator (LLRE). For homogeneous samples\, NWE demonstrates an inapp ropriate bias in points where the regressor probability density function ( PDF) has discontinuity (jump points). For such a scenario\, the LLRE stand s as a remedy\, having a significantly smaller bias.\n\nIn this talk\, we consider a modification of NWE (mNWE) and LLRE (mLLRE) 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 regressor's PDF respectively \, whereas the mLLRE preserves the same rate of convergence for both cases .\n LOCATION:https://researchseminars.org/talk/AMIS/6/ URL:https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE3Q T09 Passcode (if necessary) 785163 END:VEVENT END:VCALENDAR