A variable selection proposal for multiple linear regression analysis
Date
2011
Authors
Steel S.J.
Uys D.W.
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Variable selection in multiple linear regression models is considered. It is shown that for the special case of orthogonal predictor variables, an adaptive pre-test-type procedure proposed by Venter and Steel [Simultaneous selection and estimation for the some zeros family of normal models, J. Statist. Comput. Simul. 45 (1993), pp. 129-146] is almost equivalent to least angle regression, proposed by Efron et al. [Least angle regression, Ann. Stat. 32 (2004), pp. 407-499]. A new adaptive pre-test-type procedure is proposed, which extends the procedure of Venter and Steel to the general non-orthogonal case in a multiple linear regression analysis. This new procedure is based on a likelihood ratio test where the critical value is determined data-dependently. A practical illustration and results from a simulation study are presented. © 2011 Copyright Taylor and Francis Group, LLC.
Description
Keywords
LARS, lasso, limited translation, pre-test selection, unbiased risk estimation
Citation
Journal of Statistical Computation and Simulation
81
12
2095
2105
81
12
2095
2105