Artículo
Minimum penalized φ-divergence estimation under model misspecification
Autor/es | Alba Fernández, María Virtudes
Jiménez Gamero, María Dolores Ariza López, Francisco Javier |
Departamento | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Fecha de publicación | 2018-04-30 |
Fecha de depósito | 2018-05-22 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España |
Identificador del proyecto | CTM2015-68276-R
MTM2017-89422-P |
Cita | Alba Fernández, M.V., Jiménez Gamero, M.D. y Ariza López, F.J. (2018). Minimum penalized φ-divergence estimation under model misspecification. Entropy, 20 (329), 1-15. |
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