Date of this Version
Journal of the American Statistical Association
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.
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.
marginal false discovery rate, model selection, stepwise regression, variable selection
Lin, D., Foster, D. P., & Ungar, L. H. (2011). VIF Regression: A Fast Regression Algorithm for Large Data. Journal of the American Statistical Association, 106 (493), 232-247. http://dx.doi.org/10.1198/jasa.2011.tm10113
Date Posted: 27 November 2017