This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 81
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #307288
Title: Model Identification and Regression Estimation with a Diverging Number of Predictors
Author(s): Zhan Wang*+ and Yuhong Yang
Companies: University of Minnesota and University of Minnesota
Address: 1293 Fifield Place, Falcon Heights, MN, 55108,
Keywords: Consistency ; Minimax-rate optimality ; Model selection ; Invariance test ; Lasso ; SCAD
Abstract:

In model selection problems where the true model is fixed and the sample size increases, it is now well known that if a model selection criterion is consistent, it cannot be minimax-rate optimal for estimation. In some situations, more variables are introduced as the sample size increases and it may be more realistic to treat the true model as a changing target when information accumulates. In this case, we prove that a conflict between model selection consistency and minimax-rate optimality of estimation still exists as long as some mild conditions are satisfied. In particular, we show that all model selection methods that have a certain invariance property are subject to this conflict.


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