Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator

Elena Castilla; Nirian Martín; Leandro Pardo; Konstantinos Zografos

doi:10.3390/e20010018

Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator

Entropy (Basel). 2017 Dec 31;20(1):18. doi: 10.3390/e20010018.

Authors

Elena Castilla¹, Nirian Martín², Leandro Pardo¹, Konstantinos Zografos³

Affiliations

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.

Abstract

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.

Keywords: Wald test statistic; Wald-type test statistics; composite likelihood; composite minimum density power divergence estimator; maximum composite likelihood estimator.