基于几何建模方法的经验零分布估计在多重假设检验的经验贝叶斯建模中的应用。
On the geometric modeling approach to empirical null distribution estimation for empirical Bayes modeling of multiple hypothesis testing.
机构信息
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
出版信息
Comput Biol Chem. 2013 Apr;43:17-22. doi: 10.1016/j.compbiolchem.2012.12.001. Epub 2012 Dec 22.
We study the geometric modeling approach to estimating the null distribution for the empirical Bayes modeling of multiple hypothesis testing. The commonly used method is a nonparametric approach based on the Poisson regression, which however could be unduly affected by the dependence among test statistics and perform very poorly under strong dependence. In this paper, we explore a finite mixture model based geometric modeling approach to empirical null distribution estimation and multiple hypothesis testing. Through simulations and applications to two public microarray data, we will illustrate its competitive performance.
我们研究了几何建模方法，用于估计多重假设检验的经验贝叶斯建模的零分布。常用的方法是基于泊松回归的非参数方法，然而，这种方法可能会受到检验统计量之间的相关性的过度影响，并且在强相关性下表现非常差。在本文中，我们探索了一种基于有限混合模型的几何建模方法来估计经验零分布和进行多重假设检验。通过模拟和对两个公共微阵列数据集的应用，我们将展示其有竞争力的性能。
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