Testing the fit of a regression model via score tests in random effects models

Abstract

This paper considers testing the goodness-of-fit of regression models. Emphasis is on a goodness-of-fit test for generalized linear models with canonical link function and known dispersion parameter. The test is based on the score test for extra variation in a random effects model. By choosing a suitable form for the dispersion matrix, a goodness-of-fit test statistic is obtained which is quite similar to test statistics based on non-parametric kernel methods. We consider the distribution of the test statistic and discuss the choice of the dispersion matrix. The testing method can handle models with continuous and discrete covariates. Corrections for bias when parameters are estimated are available and extensions to models with unknown dispersion parameters, and more general nonlinear models are discussed. The proposed goodness-of-fit method is demonstrated in a simulation study and on real data of bone marrow transplant patients. The individual contributions of observations to the test statistic are used to perform residual analyses.