Divergence and Model Adequacy, A Semiparametric Case Study

Abstract

Adequacy for estimation between an inferential method and a model can be de{\ldots}ned through two main requirements: {\ldots}rstly the inferential tool should de{\ldots}ne a well posed problem when applied to the model; secondly the resulting statistical procedure should produce consistent estimators. Conditions which entail these analytical and statistical issues are considered in the context when divergence based inference is applied for smooth semiparametric models under moment restrictions. A discussion is also held on the choice of the divergence, extending the classical parametric inference to the estimation of both parameters of interest and of nuisance. Arguments in favor of the omnibus choice of the L 2 and Kullback Leibler choices as presented in [16] are discussed and motivation for the class of power divergences de{\ldots}ned in [5] is presented in the context of the present semi parametric smooth models. A short simulation study illustrates the method.