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Open AccessArticle

Minimum Penalized ϕ-Divergence Estimation under Model Misspecification

1
Departamento de Estadística e Investigación Operativa, Universidad de Jaén, 23071, Jaén, Spain
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Departamento de Estadística e Investigación Operativa, Universidad de Sevilla, 41012, Sevilla, Spain
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Departamento de Ingeniería Cartográfica, Geodésica y Fotogrametría, Universidad de Jaén, 23071, Jaén, Spain
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(5), 329; https://doi.org/10.3390/e20050329
Received: 8 March 2018 / Revised: 22 April 2018 / Accepted: 27 April 2018 / Published: 30 April 2018
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. View Full-Text
Keywords: minimum penalized ϕ-divergence estimator; consistency; asymptotic normality; goodness-of-fit; bootstrap distribution estimator; thematic quality assessment minimum penalized ϕ-divergence estimator; consistency; asymptotic normality; goodness-of-fit; bootstrap distribution estimator; thematic quality assessment
MDPI and ACS Style

Alba-Fernández, M.V.; Jiménez-Gamero, M.D.; Ariza-López, F.J. Minimum Penalized ϕ-Divergence Estimation under Model Misspecification. Entropy 2018, 20, 329.

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