B1667
Title: Goodness-of-fit tests for variance function in regression models
Authors: Sandie Ferrigno - INRIA Nancy and University Nancy Lorraine (France) [presenting]
Marie-Jose Martinez - University of Grenoble (France)
Abstract: Many goodness-of-fit tests have been developed to assess the different assumptions of a regression model. Most of them are ``directional'' in that they detect departures from a given assumption of the model. Other tests are ``global'' in that they assess whether a model fits a dataset on all its assumptions. We focus on the task of choosing the structural part of the variance function in the (possibly heteroscedastic) regression model. We consider two nonparametric ``directional'' tests and one nonparametric ``global'' test, all based on generalizations of the Cramer-von Mises statistic. A simulation study is carried out to compare the three test methods in terms of statistical significance and power function. The implementation of such statistical tests requires the use of wild bootstrap methods.
