Weighted likelihood estimating equations: The discrete case with applications to logistic regression

Abstract

We discuss a method of weighting the likelihood equations with the aim of obtaining fully efficient and robust estimators. We discuss the case of discrete probability models using several weighting functions. If the weight functions generate increasing residual adjustment functions then the method provides a link between the maximum likelihood score equations and minimum disparity estimation, as well as a set of diagnostic weights and a goodness of fit criterion. However, when the weights do not generate increasing residual adjustment functions a selection criterion is needed to obtain the robust root. The weight functions discussed in this paper do not automatically downweight a proportion of the data; an observation is significantly downweighted only if it is inconsistent with the assumed model. At the true model, therefore, the proposed estimating equations behave like the ordinary likelihood equations. We apply our results to several discrete models; in addition, a toxicology experiment illustrates the method in the context of logistic regression.