Restricted polynomial regression

A simple method of extending linear models to models that include non-linear predictor effects and interactions is investigated. The new technique builds a polynomial model by using sums of products of linear functions of the predictor variables (or other basis functions). The sum of products expansion more effectively controls the number of parameters than a general kth-order polynomial model. Examples are given for regression and classification. Finally, extensions to more general nonparametric modeling are discussed.