Profiled adaptive Elastic-Net procedure for partially linear models with high-dimensional covariates

Baicheng Chen, Yao Yu, Hui Zou, Hua Liang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We study variable selection for partially linear models when the dimension of covariates diverges with the sample size. We combine the ideas of profiling and adaptive Elastic-Net. The resulting procedure has oracle properties and can handle collinearity well. A by-product is the uniform bound for the absolute difference between the profiled and original predictors. We further examine finite sample performance of the proposed procedure by simulation studies and analysis of a labor-market dataset for an illustration.

Original languageEnglish (US)
Pages (from-to)1733-1745
Number of pages13
JournalJournal of Statistical Planning and Inference
Volume142
Issue number7
DOIs
StatePublished - Jul 2012

Bibliographical note

Funding Information:
Liang's research was partially supported by NSF grant DMS-1007167 .

Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.

Keywords

  • Adaptive regularization
  • Elastic-Net
  • High dimensionality
  • Model selection
  • Oracle property
  • Presmoothing
  • Semiparametric model
  • Shrinkage methods

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