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Open AccessArticle

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

1
Interdisciplinary Mathematics Institute and Department of Statistics and O.R. I, Complutense University of Madrid, 28040 Madrid, Spain
2
Interdisciplinary Mathematics Institute and Department of Financial and Actuarial Economics & Statistics, Complutense University of Madrid, 28003 Madrid, Spain
3
Department of Mathematics, University of Ioannina, 45110 Ioannina, Greece
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(3), 270; https://doi.org/10.3390/e22030270
Received: 22 January 2020 / Revised: 17 February 2020 / Accepted: 25 February 2020 / Published: 27 February 2020
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. View Full-Text
Keywords: composite likelihood; composite minimum density power divergence estimators; model selection composite likelihood; composite minimum density power divergence estimators; model selection
MDPI and ACS Style

Castilla, E.; Martín, N.; Pardo, L.; Zografos, K. Model Selection in a Composite Likelihood Framework Based on Density Power Divergence. Entropy 2020, 22, 270. https://doi.org/10.3390/e22030270

AMA Style

Castilla E, Martín N, Pardo L, Zografos K. Model Selection in a Composite Likelihood Framework Based on Density Power Divergence. Entropy. 2020; 22(3):270. https://doi.org/10.3390/e22030270

Chicago/Turabian Style

Castilla, Elena; Martín, Nirian; Pardo, Leandro; Zografos, Konstantinos. 2020. "Model Selection in a Composite Likelihood Framework Based on Density Power Divergence" Entropy 22, no. 3: 270. https://doi.org/10.3390/e22030270

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