Building and using semiparametric tolerance regions for parametric multinomial models

TL;DR

This paper introduces a semiparametric approach using tubular neighborhoods based on Kullback-Leibler distance to assess and test the fit of parametric multinomial models, with improved bootstrap-based inference.

Contribution

It develops a novel tubular neighborhood framework for multinomial models and proposes a likelihood ratio test with bootstrap calibration for model fit assessment.

Findings

Likelihood ratio test for model fit index

Bootstrap method improves confidence interval accuracy

Framework accommodates model misspecification

Abstract

We introduce a semiparametric ``tubular neighborhood'' of a parametric model in the multinomial setting. It consists of all multinomial distributions lying in a distance-based neighborhood of the parametric model of interest. Fitting such a tubular model allows one to use a parametric model while treating it as an approximation to the true distribution. In this paper, the Kullback--Leibler distance is used to build the tubular region. Based on this idea one can define the distance between the true multinomial distribution and the parametric model to be the index of fit. The paper develops a likelihood ratio test procedure for testing the magnitude of the index. A semiparametric bootstrap method is implemented to better approximate the distribution of the LRT statistic. The approximation permits more accurate construction of a lower confidence limit for the model fitting index.