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DTSTAMP:20250808T013043Z
DESCRIPTION:Title: Group penalized expectile regression\n\nAbstract: The as
 ymmetric least squares (Expectile) regression allows to estimate unknown e
 xpectiles of the conditional distribution of a response variable as a func
 tion of a set of predictors and can handle heteroscedasticity issues. High
  dimensional data\, such as omics data\, are error prone and usually displ
 ay heterogeneity. Such heterogeneity is often of scientific interest. In t
 his work\, we propose the Group Penalized Expectile Regression (GPER) appr
 oach\, under high dimensional settings. GPER considers implementation of s
 parse expectile regression with group Lasso penalty and the group non-conv
 ex penalties SCAD/ MCP. However\, GPER may fail to tell which groups varia
 bles are important for the conditional mean and which groups variables are
  important for the conditional scale/variance. To that end\, we further pr
 opose a COupled Group Penalized Expectile Regression (COGPER) regression w
 hich can be efficiently solved by an algorithm similar to that for solving
  GPER. We establish theoretical properties of of the proposed approaches. 
 In particular\, GPER and COGPER using the SCAD penalty or MCP is shown to 
 consistently identify the two important subsets for the mean and scale sim
 ultaneously. We demonstrate the empirical performance of GPER and COGPER b
 y simulated and real data.\n
DTSTART:20200220T203000Z
DTEND:20200220T213000Z
LOCATION:Room PK-5115 \, CA\, Pavillon President-Kennedy\, 201 Ave. Preside
 nt-Kennedy
SUMMARY:Mohamed Ouhourane\, UQAM
URL:/mathstat/channels/event/mohamed-ouhourane-uqam-32
 0456
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