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基于最小密度功率散度估计器的复合似然方法。

Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator.

作者信息

Castilla Elena, Martín Nirian, Pardo Leandro, Zografos Konstantinos

机构信息

Department of Statistics and O.R. I, Complutense University of Madrid, 28040 Madrid, Spain.

Department of Statistics and O.R. II, Complutense University of Madrid, 28003 Madrid, Spain.

出版信息

Entropy (Basel). 2017 Dec 31;20(1):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. The composite minimum density power divergence estimator is also introduced, and its asymptotic properties are studied.

摘要

在本文中,通过使用复合最小密度功率散度估计量而非复合最大似然估计量,考虑了用于复合似然的稳健版Wald检验统计量。这个新的检验统计量族将被称为Wald型检验统计量。考虑了检验简单原假设和复合原假设的问题,并基于模拟研究对稳健性进行了研究。还引入了复合最小密度功率散度估计量,并研究了其渐近性质。

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