Minimum Penalized ϕ-Divergence Estimation under Model Misspecification

Minimum Penalized ϕ-Divergence Estimation under Model Misspecification

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...

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Bibliographic Details
Main Authors: M. Virtudes Alba-Fernández, M. Dolores Jiménez-Gamero, F. Javier Ariza-López
Format: Article
Language:English
Published: MDPI AG 2018-04-01
Series:Entropy
Subjects:
minimum penalized ϕ-divergence estimator
consistency
asymptotic normality
goodness-of-fit
bootstrap distribution estimator
thematic quality assessment
Online Access:http://www.mdpi.com/1099-4300/20/5/329
Description
Summary: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.
ISSN:1099-4300

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