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Título:
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Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
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Autores:
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Castilla González, Elena ;
Martín Apaolaza, Nirian ;
Pardo Llorente, Leandro ;
Zografos, Konstantinos
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Tipo de documento:
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texto impreso
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Editorial:
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https://www.mdpi.com/, 2020
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Dimensiones:
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application/pdf
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Nota general:
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cc_by
info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = Publicado
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Materia = Ciencias: Matemáticas
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Materia = Ciencias: Matemáticas: Probabilidades
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Tipo = Artículo
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Resumen:
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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 ?. 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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En línea:
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https://eprints.ucm.es/id/eprint/63188/1/entropy-22-00270-v3.pdf
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