Statistics Papers

Document Type

Journal Article

Date of this Version

2011

Publication Source

Journal of the American Statistical Association

Volume

106

Issue

493

Start Page

232

Last Page

247

DOI

10.1198/jasa.2011.tm10113

Abstract

We propose a fast and accurate algorithm, VIF regression, for doing feature selection in large regression problems. VIF regression is extremely fast: it uses a one-pass search over the predictors, and a computationally efficient method of testing each potential predictor for addition to the model. VIF regression provably avoids model over-fitting, controlling marginal False Discovery Rate (mFDR). Numerical results show that it is much faster than any other published algorithm for regression with feature selection, and is as accurate as the best of the slower algorithms.

Copyright/Permission Statement

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 01 Jan 2012, available online: http://wwww.tandfonline.com/10.1198/jasa.2011.tm10113.

Keywords

marginal false discovery rate, model selection, stepwise regression, variable selection

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Date Posted: 27 November 2017