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Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator
1
Department of Statistics and O.R. I, Complutense University of Madrid, 28040 Madrid, Spain
2
Department of Statistics and O.R. II, 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 2018, 20(1), 18; https://doi.org/10.3390/e20010018
Received: 6 November 2017 / Revised: 26 December 2017 / Accepted: 28 December 2017 / Published: 31 December 2017
(This article belongs to the Special Issue New Developments in Statistical Information Theory Based on Entropy and Divergence Measures)
In this paper, a robust version of the Wald test statistic for composite likelihood is considered by using the composite minimum density power divergence estimator instead of the composite maximum likelihood estimator. This new family of test statistics will be called Wald-type test statistics. The problem of testing a simple and a composite null hypothesis is considered, and the robustness is studied on the basis of a simulation study. The composite minimum density power divergence estimator is also introduced, and its asymptotic properties are studied.
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Keywords:
composite likelihood; maximum composite likelihood estimator; Wald test statistic; composite minimum density power divergence estimator; Wald-type test statistics
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MDPI and ACS Style
Castilla, E.; Martín, N.; Pardo, L.; Zografos, K. Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator. Entropy 2018, 20, 18.
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