On the Impact of Variable Selection in Fitting Regression Equations

Abstract

Consider developing a regression model in a context where substantive theory is weak. Search procedures are often used to develop the equation: eg, fitting the equation, dropping insignificant variables, and refitting. As is well known, this can seriously distort the conventional goodness-of-fit statistics. Furthermore, the bootstrap and jackknife may not help in high-dimensional cases.