Testing lack of fit in multiple regression

Abstract

We study lack-of-fit tests based on orthogonal series estimators. A common feature of these tests is that they are functions of score statistics that employ data-driven model dimensions. The criteria used to select the dimension are score-based versions of AIC and BIC . The tests can be applied in a wide variety of settings, including both continuous and discrete data. With two or more covariates, a model sequence, i.e. a path in the alternative models space, has to be chosen. Critical points and p -values of the lack-of-fit tests can be obtained via asymptotic distribution theory or by use of the bootstrap. Data examples and a simulation study illustrate the applicability of the tests.