Testing equivalence to binary generalized linear models with application to logistic regression

Abstract

We introduce a new equivalence test to show sufficiently good agreement of observed data with a binary generalized linear model (GLM). The test statistic is constructed via the minimum distance method. The test is developed for the important special case where all covariates are categorical. The critical values can be calculated using an asymptotic approximation or by means of bootstrapping. The application of the test to logistic regression is illustrated on two real data sets. The finite sample performance of the proposed test is studied by simulations which are based on these two data sets.