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UID:20251121T070039EST-6282caGHev@132.216.98.100
DTSTAMP:20251121T120039Z
DESCRIPTION:Sparse Envelope Model: Efficient Estimation and Response Variab
 le Selection in Multivariate Linear Regression\n\n \n\n\n	\n		\n			\n				The envelop
 e model is a method for efficient estimation in multivariate linear regres
 sion. In this article\, we propose the sparse envelope model\, which is mo
 tivated by applications where some response variables are invariant to cha
 nges of the predictors and have zero regression coefficients. The envelope
  estimator is consistent but not sparse\, and in many situations it is imp
 ortant to identify the response variables for which the regression coeffic
 ients are zero. The sparse envelope model performs variable selection on t
 he responses and preserves the efficiency gains offered by the envelope mo
 del. Response variable selection arises naturally in many applications\, b
 ut has not been studied as thoroughly as predictor variable selection. In 
 this article\, we discuss response variable selection in both the standard
  multivariate linear regression and the envelope contexts. In response var
 iable selection\, even if a response has zero coefficients\, it still shou
 ld be retained to improve the estimation efficiency of the nonzero coeffic
 ients. This is different from the practice in predictor variable selection
 . We establish consistency\, the oracle property and obtain the asymptotic
  distribution of the sparse envelope estimator.\n			\n		\n	\n\n
DTSTART:20170210T203000Z
DTEND:20170210T213000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Zhihua Su Assistant Professor of Statistics University of Florida
URL:/mathstat/channels/event/zhihua-su-assistant-profe
 ssor-statistics-university-florida-265605
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