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UID:4941@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20231106T140000
DTEND;TZID=Europe/Paris:20231106T150000
DTSTAMP:20240524T072506Z
URL:https://www.i2m.univ-amu.fr/evenements/shrinkage-for-extreme-partial-l
 east-squares/
SUMMARY: (...): Shrinkage for Extreme Partial Least Squares
DESCRIPTION:: This research focuses on dimension-reduction techniques for m
 odeling conditional extreme values. Specifically\, we investigate the idea
  that extreme values of a response variable can be explained by nonlinear 
 functions derived from linear projections of an input random vector. In th
 is context\, the estimation of projection directions is examined\, as appr
 oached by the Extreme Partial Least Squares (EPLS) method--an adaptation o
 f the original Partial Least Squares (PLS) method tailored to the extreme-
 value framework. Further\, a novel interpretation of EPLS directions as ma
 ximum likelihood estimators is introduced\, utilizing the von Mises-Fisher
  distribution applied to hyperballs. The dimension reduction process is en
 hanced through the Bayesian paradigm\, enabling the incorporation of prior
  information into the projection direction estimation. The maximum a poste
 riori estimator is derived in two specific cases\, elucidating it as a reg
 ularization or shrinkage of the EPLS estimator. We also establish its asym
 ptotic behavior as the sample size approaches infinity. A simulation data 
 study is conducted in order to assess the practical utility of our propose
 d method. This clearly demonstrates its effectiveness even in moderate dat
 a problems within high-dimensional settings. Furthermore\, we provide an i
 llustrative example of the method's applicability using French farm income
  data\, highlighting its efficacy in real-world scenarios.
CATEGORIES:Séminaire,Statistique
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DTSTART:20231029T020000
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