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UID:event-10441@www.amse-aixmarseille.fr
DTSTAMP:20260421T230643Z
CREATED:20260421T230643Z
LAST-MODIFIED:20260421T230643Z
STATUS:CONFIRMED
SEQUENCE:0
SUMMARY:big data and econometrics seminar - Gilles Stupfler
DTSTART:20240312T130000Z
DTEND:20240312T143000Z
DESCRIPTION:The objective of extremal regression is to estimate and infer q
 uantities describing the tail of a conditional distribution. Examples of su
 ch quantities include quantiles and expectiles\, and the regression version
  of the Expected Shortfall. Traditional regression estimators at the tails 
 typically suffer from instability and inconsistency due to data sparseness\
 , especially when the underlying conditional distributions are heavy-tailed
 . Existing approaches to extremal regression in the heavy-tailed case fall 
 into two main categories: linear quantile regression approaches and\, at th
 e opposite\, nonparametric approaches. They are also typically restricted t
 o i.i.d. data-generating processes. I will here give an overview of a recen
 t series of papers that discuss extremal regression methods in location-sca
 le regression models (containing linear regression quantile models) and non
 parametric regression models. Some key novel results include a general tool
 box for extreme value estimation in the presence of random errors and joint
  asymptotic normality results for nonparametric extreme conditional quantil
 e estimators constructed upon strongly mixing data. Joint work with A. Daou
 ia\, S. Girard\, M. Oesting and A. Usseglio-Carleve.\\n\\nContact: Michel L
 ubrano: michel.lubrano[at]univ-amu.frPierre Michel: pierre.michel[at]univ-
 amu.fr\n\nPlus d'informations: https://www.amse-aixmarseille.fr/en/events/g
 illes-stupfler-2
LOCATION:Îlot Bernard du Bois - Salle 21\, AMU - AMSE\, 5-9 boulevard Maur
 ice Bourdet\, 13001 Marseille
URL;VALUE=URI:https://www.amse-aixmarseille.fr/en/events/gilles-stupfler-2
CONTACT:Michel Lubrano: michel.lubrano[at]univ-amu.frPierre Michel:&nbsp\;p
 ierre.michel[at]univ-amu.fr
TRANSP:OPAQUE
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