A Simulation Study of Alternatives to Ordinary Least Squares

Abstract Estimated regression coefficients and errors in these estimates are computed for 160 artificial data sets drawn from 160 normal linear models structured according to factorial designs. Ordinary multiple regression (OREG) is compared with 56 alternatives which pull some or all estimated regression coefficients some or all the way to zero. Substantial improvements over OREG are exhibited when collinearity effects are present, noncentrality in the original model is small, and selected true regression coefficients are small. Ridge regression emerges as an important tool, while a Bayesian extension of variable selection proves valuable when the true regression coefficients vary widely in importance.