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 of t...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/74879 |
| Acesso em linha: | https://hdl.handle.net/11441/74879 https://doi.org/10.3390/e20050329 |
| Access Level: | acceso abierto |
| Palavra-chave: | Minimum penalized φ-divergence estimator Consistency Asymptotic normality Goodness-of-fit Bootstrap distribution estimator Thematic quality assessment |
| Resumo: | 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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