Model Selection in a Composite Likelihood Framework Based on Density Power Divergence

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 <inline-formula> <math display="inline"> <sem...

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Bibliographic Details
Main Authors: Elena Castilla, Nirian Martín, Leandro Pardo, Konstantinos Zografos
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Entropy
Subjects:
composite likelihood
composite minimum density power divergence estimators
model selection
Online Access:https://www.mdpi.com/1099-4300/22/3/270
Description
Summary: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 <inline-formula> <math display="inline"> <semantics> <mi>&#945;</mi> </semantics> </math> </inline-formula>. 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.
ISSN:1099-4300

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