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UID:20261106T155035EST-9025Ka3rGU@132.216.98.100
DTSTAMP:20261106T205035Z
DESCRIPTION:Title: Using transformations of variables to improve inference
 \n\nAbstract: In Geenens\, Charpentier & Paindaveine (2017)\, use used pro
 bit transformations 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
  dimension. The idea of transforming variables in the univariate context w
 as also used in Charpentier & Oulidi (2009)\, to improve quantile estimati
 on. In those articles\, the idea is either to transform variables to 'norm
 alize' them\, or to 'uniformize' them. This can be used to improve density
  estimation\, as well as functionals of that distribution. We will see in 
 this talk recent results obtained with Emmanuel Flachaire\, in the context
  of inequality indices and risk measures.\n
DTSTART:20181128T203000Z
DTEND:20181128T203000Z
LOCATION:Room PK-5115 \, CA\, Pavillon President-Kennedy
SUMMARY:Arthur Charpentier\, UQAM
URL:https://www.mcgill.ca/mathstat/channels/event/arthur-charpentier-uqam-2
 92147
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