Shrinkage and model selection with correlated variables via weighted fusion

Authors:
Z. John Daye;X. Jessie Jeng
Affiliations:
Department of Statistics, Purdue University, West Lafayette, IN 47907, USA;Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
Venue:
Computational Statistics & Data Analysis
Year:
2009

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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.