RE: order of covariate inclusion -> avoiding stepwise approaches

From: Ken.Kowalski@pfizer.com Subject: RE: [NMusers] order of covariate inclusion -> avoiding stepwise approaches Date: 9/26/2003 10:25 AM Marc, I agree with you that the full model is a better basis for making inference such as in constructing confidence intervals. However, I'm not ready to throw away development of parsimonious models either. When the parsimonious model has considerably fewer parameters than the full model, it may provide better predictions by smoothing out some of the noise in the full model predictions since many of the parameter estimates from the full model are just estimating noise. Many view development of a full model as a means to an end...I don't. I like to report out results for the base (no covariates), full, and final parsimonious models and use each of these models for different purposes. The latter, parsimonious models, I typically like to use for predictions. That being said, I think we need to be cautious when using stepwise procedures or any model building procedure (including WAM) particularly when we are dealing with a high degree of collinearity among the covariates. Forward selection procedures can lull one into a false sense of security if the modeler is not cognizant of the collinearity. Whereas building a full model will often require the modeler to deal with it. Ken