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:20261115T002147Z
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:20261115T002147Z
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:20261115T002147Z
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:20261115T002147Z
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:20261115T002147Z
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:20261115T002147Z
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\n\nAbstract\nFinite
mixture models naturally 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’ distributions. In the classical finite mixture models (FMM
) the concentrations of the components in the mixture (mixing probabilitie
s) are the same for all observations. In a more flexible mixture with vary
ing concentrations model (MVC)\, the concentrations are different for diff
erent observations.\n\nRegression models are typically applied to describe
dependency between different numerical variables of one subject. In the c
ase of homogeneous sample there exist many non-parametric estimators of th
e regression function\, such as the Nadaraya-Watson estimator (NWE) and lo
cal linear regression estimator (LLRE). For homogeneous samples\, NWE demo
nstrates an inappropriate bias in points where the regressor probability d
ensity function (PDF) has discontinuity (jump points). For such a scenario
\, the LLRE stands as a remedy\, having a significantly smaller bias.\n\nI
n this talk\, we consider a modification of NWE (mNWE) and LLRE (mLLRE) fo
r the estimation of the regression function of some MVC component. We will
show that under suitable assumptions\, the modified estimators are asympt
otically normal. Moreover\, the rate of convergence for the mNWE is differ
ent at different points of continuity and discontinuity of the regressor's
PDF respectively\, whereas the mLLRE preserves the same rate of convergen
ce for both cases.\n
LOCATION:https://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:20261115T002147Z
UID:AMIS/7
DESCRIPTION:Title: Imp
licit bias in optimization algorithms\nby Dr. Yurii Malitskyi (Assista
nt Professor) (Faculty of Mathematics\, University of Vienna\, Austria) as
part of Asymptotic Methods in Statistics\n\n\nAbstract\nIn this talk\, we
will explore the role of implicit bias in optimization algorithms. Implic
it bias refers to the tendency of an algorithm to converge to a specific s
olution even in the absence of an explicit regularization term in its form
ulation. In other words\, implicit bias emerges from how the algorithm its
elf interacts with the objective function it’s trying to minimize. We wi
ll demonstrate this concept through a few surprising applications.\n\nAbst
ract. In this talk\, we will explore the role of implicit bias in optimiz
ation algorithms. Implicit bias refers to the tendency of an algorithm to
converge to a specific solution even in the absence of an explicit regular
ization term in its formulation. In other words\, implicit bias emerges fr
om how the algorithm itself interacts with the objective function it’s t
rying to minimize. We will demonstrate this concept through a few surprisi
ng applications.\n
LOCATION:https://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:20261115T002147Z
UID:AMIS/8
DESCRIPTION:Title: On
the Statistical Properties of Impulse Response Function Estimators\nby
Prof. Dr. Iryna Rozora (Prof. Dr. Iryna Rozora (Head of Department of App
lied Statistics\, Faculty of Cybernetics and Computer Sciences\, Taras She
vchenko National University of Kyiv) as part of Asymptotic Methods in Stat
istics\n\n\nAbstract\nEstimating stochastic linear systems using Impulse R
esponse Functions (IRFs) is a fundamental problem with broad relevance acr
oss multiple disciplines. IRFs describe how a system responds to external
inputs\, providing essential insights into system behavior and control. Su
ch analyses have important applications in signal processing\, control the
ory\, econometrics\, oceanography\, and related fields.\nIn this talk\, I
will focus on the estimation of IRFs for Single-Input Single-Output (SISO)
systems. We consider a time-invariant continuous linear system driven by
zero-mean stationary Gaussian input processes. The aim of the study is to
develop a method for estimating the unknown IRF based on observations of b
oth the system’s input and output signals. Our approach is based on a cr
oss-correlogram estimator\, which computes the correlation between the inp
ut and output processes. We establish conditions for asymptotic unbiasedne
ss and consistency\, analyze the rate of convergence\, and propose a stati
stical goodness-of-fit test for validating the estimated IRF. The theoreti
cal results are supported by numerical simulations.\n
LOCATION:https://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:20261115T002147Z
UID:AMIS/9
DESCRIPTION:Title: Ada
ptive radar system configuration tuning\nby Dr. Andriy Malenko (senior
researcher of V. M. Glushkov Institute of Cybernetics NAS of Ukraine) as
part of Asymptotic Methods in Statistics\n\n\nAbstract\nThe talk focuses o
n an algorithm for parameters tuning for radar-based sensing systems for m
otion monitoring. The algorithm adapts parameters for better system perfor
mance according to the estimated motion zone properties\, i.e. mean and ma
ximum distance to objects and their velocities. A new approach for determi
nation of radar height position and tilt angle is proposed and experimenta
lly tested. The method uses radar measurement points cloud only and may be
applied for 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 degrees. Height estimation error depends on moving object height and
is less than 1 m. The main goal of algorithm is estimation of motion zone
and corresponding adaptive change of radar-system configuration parameters
for optimizing system's field of view and performance. The proposed appro
ach may be used for indoor and outdoor motion detection and activity class
ification in applications such as health diagnostics\, health monitoring\,
surveillance\, occupancy sensing\, and automotive.\n
LOCATION:https://researchseminars.org/talk/AMIS/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Prof. Dr. Igor Mandel (NJ\, USA)
DTSTART:20260902T150000Z
DTEND:20260902T160000Z
DTSTAMP:20261115T002147Z
UID:AMIS/10
DESCRIPTION:Title: Hu
man vs. Computational Aesthetic Evaluation in Statistical Models of Art Pr
ices\nby Prof. Dr. Igor Mandel (NJ\, USA) as part of Asymptotic Method
s in Statistics\n\n\nAbstract\nThis presentation provides a concise survey
of the topic\, spanning from hedonic regression models developed over the
last 15-20 years to contemporary models based on automatic feature extrac
tion and the extensive application of artificial intelligence. Particular
emphasis is placed on the comparison between human and computational asses
sments of the aesthetic characteristics of paintings and their relationshi
p to price. In addition to the mathematical aspects of the problem\, numer
ous illustrative examples of painting evaluations will be presented.\n[1]
Igor Mandel. Predicting Art Market Prices with AI-Based Aesthetic Evaluati
on\, SSRN\, 2026.\n[2] Igor Mandel\, Stefano Mastandrea\, Ivan Shamshurin\
, Kirill Rachenkov. Beauty or money? Statistical analysis. Model Assisted
Statistical Applications\, Volume 16\, Issue 2\, 2021.\n[3] Igor Mandel. A
esthetic\, Art-Historical and Economic Values in Painting: Empirical Study
\, in: Integrative Explorations of the Creative Mind\, Cambridge Scholars
Publishing\, 2019.\n\nNice paintings will be shown. Prof. Dr. Igor Mandel
is a well-known expert in data processing.\n
LOCATION:https://researchseminars.org/talk/AMIS/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dr. Yevhen Turchyn (Oles Honchar National University of Dnipro (Uk
raine))
DTSTART:20260909T140000Z
DTEND:20260909T150000Z
DTSTAMP:20261115T002147Z
UID:AMIS/11
DESCRIPTION:Title: Th
e Arctan-Tan-G Family of Distributions: Properties and Applications\nb
y Dr. Yevhen Turchyn (Oles Honchar National University of Dnipro (Ukraine)
) as part of Asymptotic Methods in Statistics\n\n\nAbstract\nWe introduce
a new family of continuous distributions\, a so-called \nArctan-Tan-G (or
ATT-G) family. Some mathematical properties of the new family are studied
including\, in particular\, asymptotics for the pdf and the cdf\,\norder s
tatistics\, and stochastic ordering. A special case of this family\, the A
TT-Weibull (ATT-W) distribution\, is discussed. The density function\, th
e quantile function\, Bowley's skewness and Moors' kurtosis are researche
d. Distribution of order statistics is obtained. The model parameters are
estimated by maximum likelihood. Usefulness of the new family is proven em
pirically by means of a real data set.\n\nDr. Yevhen Tiurchyn is Associate
Professor of Department of Statistics & Probability Theory in Oles Honcha
r National University of Dnipro (Ukraine).\n
LOCATION:https://researchseminars.org/talk/AMIS/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dr. Ievhen Turchyn (Oles Honchar National University of Dnipro (Uk
raine))
DTSTART:20260916T140000Z
DTEND:20260916T150000Z
DTSTAMP:20261115T002147Z
UID:AMIS/12
DESCRIPTION:Title: Th
e Arctan-Tan-G Family of Distributions: Properties and Applications\nb
y Dr. Ievhen Turchyn (Oles Honchar National University of Dnipro (Ukraine)
) as part of Asymptotic Methods in Statistics\n\n\nAbstract\nWe introduce
a new family of continuous distributions\, a so-called \nArctan-Tan-G (or
ATT-G) family. Some mathematical properties of the new family are studied
including\, in particular\, asymptotics for the pdf and the cdf\,\norder s
tatistics\, and stochastic ordering. A special case of this family\, the A
TT-Weibull (ATT-W) distribution\, is discussed. The density function\, th
e quantile function\, Bowley's skewness and Moors' kurtosis are researche
d. Distribution of order statistics is obtained. The model parameters are
estimated by maximum likelihood. Usefulness of the new family is proven em
pirically by means of a real data set.\n\nThe talk dated September 9 is sh
ifted to September 16.\n
LOCATION:https://researchseminars.org/talk/AMIS/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:MS student Oleksandr Liubimov (Taras Shevchenko National Universit
y of Kyiv and EPFL\, Lausanne\, Switzerland)
DTSTART:20260925T140000Z
DTEND:20260925T150000Z
DTSTAMP:20261115T002147Z
UID:AMIS/13
DESCRIPTION:Title: Po
lynomial regression under a mixture of classical and Berkson errors with u
nknown moments of the latter error\nby MS student Oleksandr Liubimov (
Taras Shevchenko National University of Kyiv and EPFL\, Lausanne\, Switzer
land) as part of Asymptotic Methods in Statistics\n\n\nAbstract\nPreviousl
y we gave consistent estimators for model parameters given the first 2d mo
ments of Berkson error\, where d is the power of the polynomial. Now we we
aken this assumption and construct new estimators for the model parameters
. In particular we estimate consistently the regression coefficients witho
ut information on moments of Berkson error.\n
LOCATION:https://researchseminars.org/talk/AMIS/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Prof. Dmytro Koroliuk (Igor Sikorsky Kyiv Polytechnic Institute\,
Faculty of Electronics)
DTSTART:20260930T153000Z
DTEND:20260930T163000Z
DTSTAMP:20261115T002147Z
UID:AMIS/14
DESCRIPTION:Title: Sp
ecifics of the frequency-statistical approach in the study of quantum obje
cts\nby Prof. Dmytro Koroliuk (Igor Sikorsky Kyiv Polytechnic Institut
e\, Faculty of Electronics) as part of Asymptotic Methods in Statistics\n\
n\nAbstract\nA quantum state does not generally specify the outcome of an
individual measurement\; it specifies the statistical structure of possibl
e outcomes for a specified measurement procedure.\nUsing several quantum c
oncepts as examples\, the significant difference between statistical appro
aches in the quantum and classical cases is demonstrated. This illustrates
who was right in the debate between Albert and Niels: does God play chess
or dice?\n
LOCATION:https://researchseminars.org/talk/AMIS/14/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Prof. Dr. Nick Cogan (Department of Mathematics Florida State Uni
versity USA)
DTSTART:20261007T140000Z
DTEND:20261007T150000Z
DTSTAMP:20261115T002147Z
UID:AMIS/15
DESCRIPTION:Title: Br
idging Mathematical Modeling and Experimental Data to Optimize Cancer Ther
apy\nby Prof. Dr. Nick Cogan (Department of Mathematics Florida State
University USA) as part of Asymptotic Methods in Statistics\n\n\nAbstract
\nOptimizing cancer treatment will increasingly rely on close coupling bet
ween experimental research and mathematical modeling. A central obstacle i
n oncology is our incomplete understanding of how therapies reshape the tu
mor microenvironment and how immune activity drives tumor regression. This
gap is especially acute in aggressive cancers such as triple-negative bre
ast cancer (TNBC)\, which lacks standard biomarkers and has limited treatm
ent options. Our collaborative work develops a quantitative framework for
studying combined immune and chemotherapeutic interventions. Using a novel
radiolabeled T-cell tracking approach\, we follow immunomodulation over t
ime and use these measurements to guide the design of optimal therapy sche
dules. In this talk\, I will present ordinary differential equation (ODE)
models of combination therapy dynamics and compare model performance with
and without T-cell data. I will also discuss broader directions for this w
ork\, including extensions to organ-level analysis and the use of optimal
control methods to refine treatment strategies. By integrating experiment
and theory\, we aim to strengthen the predictive power of cancer treatment
models and move toward more effective\, personalized therapeutic protocol
s.\n\nhttp://www.math.fsu.edu/~cogan\n
LOCATION:https://researchseminars.org/talk/AMIS/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dr. Sc. (Technics) Serhii Zabolotnii (ORCID 0000-0003-0242-2234) (
Cherkasy State Business College)
DTSTART:20261014T140000Z
DTEND:20261014T150000Z
DTSTAMP:20261115T002147Z
UID:AMIS/16
DESCRIPTION:Title: Se
cond-Order Least Squares and the Polynomial Maximization Method: An Estima
ting-Equation Identity and a Higher-Degree Efficiency Reserve\nby Dr.
Sc. (Technics) Serhii Zabolotnii (ORCID 0000-0003-0242-2234) (Cherkasy Sta
te Business College) as part of Asymptotic Methods in Statistics\n\n\nAbst
ract\nFor linear regression with i.i.d. homoskedastic errors\, optimally w
eighted second-order least squares (SLS) is the degree-two polynomial maxi
mization method (PMM) estimating equation: both select the same optimal co
mbination of the residual e and of e^2 - sigma^2\, share one influence fun
ction\, and attain the slope variance c_2 g_2 / N\, where g_2 = 1 - gamma_
3^2 / (2 + gamma_4) depends only on the skewness and kurtosis of the error
law. The identity is one of estimating equations\, not merely of asymptot
ic variances: the Kunchenko centered-correlation matrix F_2 is the SLS wei
ghting operator after a change of variables\, and the design-dependent fac
tors of the SLS residual cancel in the slope score. The identity stops whe
re the optimal combination depends on the design\; outside that scope cond
itional SLS is the more general object. Within the polynomial moment class
\, degree three holds an efficiency reserve: on four right-skewed laws the
cubic direction adds between 30% and 50% asymptotic efficiency\, and unde
r symmetric platykurtic errors it is the only informative direction\, sinc
e g_2 = 1 there. The reserve belongs to the degree-three span\, which an e
fficient generalized method of moments estimator also attains\; the PMM no
rmal system is the low-dimensional route to it\, and the difference is vis
ible at n <= 200. The talk gives the asymptotic law of the feasible plug-i
n estimator\, a bootstrap pretest for choosing the degree\, Lean 4 checks
of the degree-specific algebra\, simulation evidence\, and a residual-cali
brated study on drilling innovations from the Utah FORGE 58-32 well.\nPrep
rint: arXiv:2606.11421\n
LOCATION:https://researchseminars.org/talk/AMIS/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dr. Sc. (Phys. & Math.)\, Associate Prof. Kostiantyn Ralchenko (Ta
ras Shevchenko National University of Kyiv and EPFL\, Lausanne\, Switzerla
nd)
DTSTART:20261104T150000Z
DTEND:20261104T160000Z
DTSTAMP:20261115T002147Z
UID:AMIS/17
DESCRIPTION:Title: As
ymptotic properties of drift parameter estimators in CKLS diffusion models
\nby Dr. Sc. (Phys. & Math.)\, Associate Prof. Kostiantyn Ralchenko (T
aras Shevchenko National University of Kyiv and EPFL\, Lausanne\, Switzerl
and) as part of Asymptotic Methods in Statistics\n\n\nAbstract\nThe Chan
–Karolyi–Longstaff–Sanders (CKLS) model is a flexible family of diff
usion models widely used in financial mathematics\, particularly for model
ling interest rates and stochastic volatility. It includes several classic
al models\, such as the Cox–Ingersoll–Ross model\, as special cases.\n
\nIn this talk\, we discuss the estimation of drift parameters in CKLS dif
fusion models based on continuous-time observations. We focus on the const
ruction of explicit estimators using the method of moments and investigate
their asymptotic properties. In particular\, we address strong consistenc
y\, asymptotic normality\, existence of moments\, and convergence in L^p u
nder suitable assumptions. We also discuss the role of the model parameter
s in the asymptotic behavior of the estimators and illustrate the theoreti
cal results with numerical simulations.\n
LOCATION:https://researchseminars.org/talk/AMIS/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:PhD student Daniel Horbunov (Taras Shevchenko National University
of Kyiv and EPFL\, Lausanne\, Switzerland)
DTSTART:20261028T150000Z
DTEND:20261028T160000Z
DTSTAMP:20261115T002147Z
UID:AMIS/18
DESCRIPTION:Title: Ne
arest neighbour bandwidth choice for nonparametric regression on mixtures
with varying concentrations\nby PhD student Daniel Horbunov (Taras Sh
evchenko National University of Kyiv and EPFL\, Lausanne\, Switzerland) as
part of Asymptotic Methods in Statistics\n\n\nAbstract\nFinite mixture mo
dels naturally arise in statistical analysis of biological and sociologica
l data. If the subpopulation which a subject belongs to is not known exact
ly\, the distribution of its variables is a mixture of the subpopulations
’ distributions. In the classical finite mixture models (FMM) the concen
trations of the components in the mixture (mixing probabilities) are the s
ame for all observations. In a more flexible mixture with varying concentr
ations model (MVC)\, the concentrations are different for different observ
ations.\n\nRegression models are typically applied to describe dependency
between different numerical variables of one subject. In the case of homog
eneous sample there exist many nonparametric estimators of the regression
function\, such as the Nadaraya-Watson estimator (NWE) and local linear re
gression estimator (LLRE). For homogeneous samples\, NWE demonstrates an i
nappropriate bias in points where the regressor probability density functi
on (PDF) has discontinuity (jump points). For such scenario\, the LLRE sta
nds as a remedy\, having a significantly smaller bias.\n\nIn case of the M
VC\, we consider a modification of NWE (mNWE) and LLRE (mLLRE) for estimat
ion of the regression function of a given component. Under suitable assump
tions\, these estimators are asymptotically normal\, which makes it possib
le to derive theoretically optimal bandwidths. However\, such bandwidths d
epend on unknown parameters of the underlying distributions and therefore
cannot be used in practice.\n\nIn this talk\, we propose an empirical band
width choice for the mNWE and mLLRE based on a modification of the nearest
neighbour technique. A simulation study shows that this approach performs
satisfactorily compared with the theoretically optimal bandwidth\, as mea
sured by the asymptotic mean squared error (aMSE) or the asymptotic mean i
ntegrated squared error (aMISE).\n
LOCATION:https://researchseminars.org/talk/AMIS/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Professor Emeritus B.L.S. Prakasa Rao (India)
DTSTART:20261111T150000Z
DTEND:20261111T160000Z
DTSTAMP:20261115T002147Z
UID:AMIS/19
DESCRIPTION:Title: As
sociated Sequences and Demimartingales: An Overview\nby Professor Emer
itus B.L.S. Prakasa Rao (India) as part of Asymptotic Methods in Statistic
s\n\n\nAbstract\nWe present an overview of the concepts of associated sequ
ences of random variables and demimartingales and give a review of their p
robabilistic properties such as laws of large numbers\, central limit theo
rems and up crossing inequalities. It is known that a martingale with re
spect to the natural sequence of sigma algebras forms a demimartingale b
ut not all demimatingales are generated in this form. It is all known tha
t partial sums of zero mean associated random variables forms a demimartin
gale but not all demimartingales are generated from such zero mean associ
ated sequences of random variables.\n
LOCATION:https://researchseminars.org/talk/AMIS/19/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Prof. Dr. Sandor Baran (Head of the Department of Applied Mathemat
ics and Probability Theory\, Faculty of Informatics\, University of Debrec
en\, Hungary)
DTSTART:20261118T150000Z
DTEND:20261118T160000Z
DTSTAMP:20261115T002147Z
UID:AMIS/20
DESCRIPTION:Title: Re
newable energy-related aspects of statistical post-processing\nby Prof
. Dr. Sandor Baran (Head of the Department of Applied Mathematics and Prob
ability Theory\, Faculty of Informatics\, University of Debrecen\, Hungary
) as part of Asymptotic Methods in Statistics\n\nInteractive livestream: h
ttps://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE3QT09
Passcode (if necessary) 785163\n\nAbstract\nBy the end of 2025\, the renew
able energy share of the global electricity capacity reached\n49.4% and th
e new installations are dominated by solar energy. Due to the high volatil
ity of\nphotovoltaic (PV) energy sources\, their successful integration in
to the electrical grid\nrequires accurate short-term PV power forecasts. P
V power is predicted from the\npredictions of solar irradiance\, where the
most advanced method is the probabilistic\napproach based on ensemble for
ecasts. However\, ensemble forecasts are often\nunderdispersive and subjec
t to systematic bias\, so they require some form of statistical\npost-proc
essing. First\, we introduce a general two-step machine learning-based app
roach\nto calibrating ensemble forecasts. In a case study based on global
horizontal irradiance\nforecasts of the operational ensemble prediction sy
stem of the Hungarian Meteorological\nService\, the predictive performance
of this novel method is compared with the forecast\nskill of the raw ense
mble and the state-of-the-art parametric approaches. Then\, we\ninvestigat
e the problem of direct post-processing of PV power ensemble forecasts. Wi
th\nthe help of power output data of seven PV power plants in Hungary and
corresponding PV\npower ensemble forecasts obtained from ensemble weather
predictions of the European\nCentre for Medium-Range Weather Forecasts\, w
e compare the skill of various modern\nparametric and nonparametric approa
ches.\nThis is a joint work with Ágnes Baran (University of Debrecen)\, N
ina Horat (Karlsruhe Institute of Technology)\, Sebastian Lerch (Marburg U
niversity\, HITS)\, and Martin János Mayer (Budapest University of Techno
logy and Economics).\n
LOCATION:https://researchseminars.org/talk/AMIS/20/
URL:https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE3Q
T09 Passcode (if necessary) 785163
END:VEVENT
END:VCALENDAR