Composite likelihood methods: Rao-type tests based on composite minimum density power divergence estimator

被引：0
作者：
E. Castilla
N. Martín
L. Pardo
K. Zografos
机构：
[1] Complutense University of Madrid,Department of Statistics and O.R. I
[2] Complutense University of Madrid,Department of Financial and Actuarial Economics and Statistics
[3] University of Ioannina,Department of Mathematics
来源：
Statistical Papers | 2021年 / 62卷
关键词：
Composite likelihood; Composite minimum density power divergence estimators; Restricted composite minimum density power divergence estimators; Rao-type test statistics;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper is aimed to present a robust extension of the classical Rao test statistic, in the context of composite likelihood ideas and methods. The Rao-type test statistics are defined on the basis of the composite minimum power divergence estimators instead of the composite maximum likelihood estimator. These Rao-type test statistics are used to test simple and composite null hypotheses. Their performance is evaluated in terms of a simulation study which concentrates to the robustness and the comparison of the Rao-type tests with the respective Wald-type tests considered in Castilla et al. (Entropy 20:18, 2018). The proposed here procedures are developed on the basis of the restricted composite minimum density power divergence estimators which are also discussed for the sake of completeness.
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页码：1003 / 1041
页数：38
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