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...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-04-01
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Series: | Entropy |
Subjects: | |
Online Access: | http://www.mdpi.com/1099-4300/20/5/329 |
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. |
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ISSN: | 1099-4300 |