Variable Selection for 1D Regression Models

Authors

David J. Olive, Southern Illinois University CarbondaleFollow
Douglas M. Hawkins, University of Minnesota - Twin Cities

Comments

Published in Technometrics, 47, 43-50.

Abstract

Variable selection, the search for j relevant predictor variables from a group of p candidates, is a standard problem in regression analysis. The class of 1D regression models is a broad class that includes generalized linear models. We show that existing variable selection algorithms, originally meant for multiple linear regression and based on ordinary least squares and Mallows’ C_p, can also be used for 1D models. Graphical aids for variable selection are also provided.

Recommended Citation

Olive, David J. and Hawkins, Douglas M. "Variable Selection for 1D Regression Models." (Feb 2005).