Shrinkage and model selection with correlated variables via weighted fusion

Z. John Daye, X. Jessie Jeng

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

In this paper, we propose the weighted fusion, a new penalized regression and variable selection method for data with correlated variables. The weighted fusion can potentially incorporate information redundancy among correlated variables for estimation and variable selection. Weighted fusion is also useful when the number of predictors p is larger than the number of observations n. It allows the selection of more than n variables in a motivated way. Real data and simulation examples show that weighted fusion can improve variable selection and prediction accuracy.

Original languageEnglish (US)
Pages (from-to)1284-1298
Number of pages15
JournalComputational Statistics and Data Analysis
Volume53
Issue number4
DOIs
StatePublished - Feb 15 2009

    Fingerprint

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this