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.

中文翻译：

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复合似然方法：基于复合最小密度功率散度估计器的 Rao 型检验

本文旨在在复合似然思想和方法的背景下，提出经典 Rao 检验统计量的稳健扩展。Rao 型检验统计量是基于复合最小功率发散估计量而不是复合最大似然估计量来定义的。这些 Rao 型检验统计量用于检验简单和复合零假设。它们的性能是根据模拟研究进行评估的，该模拟研究集中在 Rao 型测试与 Castilla 等人考虑的相应 Wald 型测试的稳健性和比较上。(熵 20:18, 2018)。此处提出的程序是在受限复合最小密度功率发散估计器的基础上开发的，为了完整性也进行了讨论。