Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α. After introducing such a criterion, some asymptotic properties are...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/7545 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/7545 |
| Access Level: | acceso abierto |
| Palabra clave: | 519.21 Composite likelihood Composite minimum density power divergence estimators Model selection Probabilidad compuesta Probabilidades Matemáticas (Matemáticas) Probabilidades (Matemáticas) 12 Matemáticas |
| Sumario: | This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α. After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion. |
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