Martingale transforms goodness-of-fit tests in regression models

Goodness of Fit Tests in Random Coefficient Regression Models]

Random coefficient regressions have been applied in a wide range of fields, from biology to economics, and constitute a common frame for several important statistical models. A nonparametric approach to inference in random coefficient models was initiated by Beran and Hall. In this paper we introduce and study goodness of fit tests for the coefficient distributions; their asymptotic behaviour u...

Goodness-of-Fit Tests for Parametri Regression Models

Goodness-of-Fit Tests for Parametric Regression Models

Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric Ž ts is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates...

Goodness-of-fit tests in mixed models

Mixed models, with both random and fixed effects, are most often estimated on the assumption that the random effects are normally distributed. In this paper we propose several formal tests of the hypothesis that the random effects and/or errors are normally distributed. Most of the proposed methods can be extended to generalized linear models where tests for non-normal distributions are of inte...

Comments on: Goodness-of-fit tests in mixed models

The authors are to be congratulated for an interesting paper which highlights the need for more formal procedures on the assessment of goodness of fit in the area of mixed models. The authors give a comprehensive overview of diagnostic techniques and goodness-of-fit tests for random effects in mixed models and propose a number of new nonparametric tests with omnibus character. Some of them are ...