Minimum Penalized ϕ-Divergence Estimation under Model Misspecification

Abstract

This paper focuses on the consequences of assuming a wrong model for multinomial data when using minimum penalized ϕ -divergence, also known as minimum penalized disparity estimators, to estimate the model parameters. These estimators are shown to converge to a well-defined limit. An application of the results obtained shows that a parametric bootstrap consistently estimates the null distribution of a certain class of test statistics for model misspecification detection. An illustrative application to the accuracy assessment of the thematic quality in a global land cover map is included.

Keywords

• minimum penalized ϕ-divergence estimator
• consistency
• asymptotic normality
• goodness-of-fit
• bootstrap distribution estimator
• thematic quality assessment