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UID:20260729T150141EDT-12780L14vH@132.216.98.100
DTSTAMP:20260729T190141Z
DESCRIPTION:Using transformations of variables to improve inference\n \nAbs
 tract: In Geenens\, Charpentier & Paindaveine (2017)\, use used probit tra
 nsformations on bivariate data to improve nonparametrics estimation of the
  copula density\, as intuited in Charpentier\, Fermanian & Scaillet (2007)
 . This probit transformation was extending Geenens (2014) in higher dimens
 ion. The idea of transforming variables in the univariate context was also
  used in Charpentier & Oulidi (2009)\, to improve quantile estimation. In 
 those articles\, the idea is either to transform variables to 'normalize' 
 them\, or to 'uniformize' them. This can be used to improve density estima
 tion\, as well as functionals of that distribution. We will see in this ta
 lk recent results obtained with Emmanuel Flachaire\, in the context of ine
 quality indices and risk measures.
DTSTART:20181129T203000Z
DTEND:20181129T213000Z
LOCATION:CA\, QC\, Montreal\, H2X 3Y7\, PK-5115\, 201\, av. du Président-Ke
 nnedy\,
SUMMARY:Arthur Charpentier\, UQAM
URL:https://www.mcgill.ca/mathstat/channels/event/arthur-charpentier-uqam-2
 92022
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