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SUMMARY:Extreme Kaplan-Meier integrals
DTSTART:20250617T141500
DTEND:20250617T151500
DTSTAMP:20260508T065955Z
UID:74ccd56ce02068d61a30d3d90d6e70335a485c95551b10cd6b7d9f7a
CATEGORIES:Conferences - Seminars
DESCRIPTION:Igor Rodionov\, University of Essex\nA novel and comprehensive
  methodology designed to tackle the challenges posed by extreme values in 
 the context of random censorship is introduced. The main focus is on\nthe 
 analysis of integrals based on the product-limit estimator of normalized u
 pper order statistics\, called extreme Kaplan–Meier integrals. These int
 egrals allow for the transparent\nderivation of various important asymptot
 ic distributional properties\, offering an alternative approach to convent
 ional plug-in estimation methods.\nNotably\, this methodology demonstrates
  robustness and wide applicability among various tail regimes. A noteworth
 y by-product is the extension of generalized Hill-type estimators of extre
 mes to encompass arbitrary tail behavior\, which is of independent interes
 t. The theoretical framework is applied to construct novel estimators for 
 real-valued extreme value indices for right-censored data. Simulation stud
 ies confirm the asymptotic results and\, in a competitor case\, mostly sho
 w superiority in mean square error. An application to brain cancer data de
 monstrates that censoring effects are properly accounted for\, even when f
 ocusing solely on tail classification.\n 
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
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