Published in Technometrics, 47, 43-50.


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’ Cp, can also be used for 1D models. Graphical aids for variable selection are also provided.