Comparison of Methods for Estimating the Proportion of Null Hypotheses�?�?�?�0 in High Dimensional Data When the Test Statistics is Continuous

Journal of Biometrics & Biostatistics

article

Isaac Dialsingh¹  Sherwin P Cedeno¹ 

[1] Department of Mathematics and Statistics, University of the West Indies, St. Augustine Campus

关键词: Family-wise error rate;    False discovery rate;    High dimensional data;    Hypothesis testing;    Multiple hypothesis testing;    Proportion of null hypotheses;   

DOI  :  10.4172/2155-6180.1000343

来源: Hilaris Publisher

【 摘 要 】

Advances in Genomics have re-energized interest in multiple hypothesis testing procedures but have simultaneously created new methodological and computational challenges. In Genomics for instance, it is now commonplace for experiments to measure expression levels in thousands of genes creating large multiplicity problems when thousands of hypotheses are to be tested simultaneously. Within this context we seek to identify differentially expressed genes, that is, genes whose expression levels are associated with a particular response or covariate of interest. The False Discovery Rate (FDR) is the preferred measure since the Family Wise Error Rates (FWERs) are usually overly restrictive. In the FDR methods, estimation of the proportion of null hypotheses (π0) is an important parameter that needs to be estimated.      In this paper, we compare the effectiveness of 12 methods for estimating π0 when the test statistics are continuous using simulated data with independent, weak dependence, and moderate dependence structures.

【 授权许可】

Unknown