在聚类数据中使用复合似然的多重比较

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2014-11-05 DOI:10.1515/ijb-2016-0004

M. Azadbakhsh, Xin Gao, H. Jankowski

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引用次数: 2

摘要

摘要研究了相关聚类数据的多重假设检验问题。针对现有基于极大似然估计的多重比较过程计算量大的问题，提出基于复合似然方法构建多重比较过程。新的测试统计量考虑了聚类内部的相关结构，计算方便。仿真研究表明，在存在簇内相关性的情况下，基于复合似然的方法可以很好地控制家族I型错误率，而忽略相关性会导致性能不稳定。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Multiple Comparisons Using Composite Likelihood in Clustered Data

Abstract We study the problem of multiple hypothesis testing for correlated clustered data. As the existing multiple comparison procedures based on maximum likelihood estimation could be computationally intensive, we propose to construct multiple comparison procedures based on composite likelihood method. The new test statistics account for the correlation structure within the clusters and are computationally convenient to compute. Simulation studies show that the composite likelihood based procedures maintain good control of the familywise type I error rate in the presence of intra-cluster correlation, whereas ignoring the correlation leads to erratic performance.

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.