Variable Selection via Subtle Uprooting

Authors

Xiaogang Su, University of Texas at El Paso

Publication Date

2012

Document Type

Article

Comments

Xiaogang Su (2015) Variable Selection Via Subtle Uprooting, Journal of Computational and Graphical Statistics, 24:4, 1092-1113, DOI: 10.1080/10618600.2014.955176

Abstract

This article proposes a variable selection method termed “subtle uprooting” for linear regression. In this proposal, variable selection is formulated into a single optimization problem by approximating cardinality involved in the information criterion with a smooth function. A technical maneuver is then employed to enforce sparsity of parameter estimates while maintaining smoothness of the objective function. To solve the resulting smooth nonconvex optimization problem, a modified Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm with established global and super-linear convergence is adopted. Both simulated experiments and an empirical example are provided for assessment and illustration. Supplementary materials for this article are available online.