Preprint Article Version 1 This version is not peer-reviewed

Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator

Version 1 : Received: 6 November 2017 / Approved: 6 November 2017 / Online: 6 November 2017 (12:59:45 CET)

A peer-reviewed article of this Preprint also exists.

Castilla, E.; Martín, N.; Pardo, L.; Zografos, K. Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator. Entropy 2018, 20, 18. Castilla, E.; Martín, N.; Pardo, L.; Zografos, K. Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator. Entropy 2018, 20, 18.

Journal reference: Entropy 2018, 20, 18
DOI: 10.3390/e20010018

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. Previously, the composite minimum density power divergence estimator is introduced and its asymptotic properties are studied.

Subject Areas

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