DoHwan Park ColloquiumThursday, October 1, 2015 - 2:45pm
Statistics Department Colloquium
Where: Close-Hipp Building, Room 364
Speaker: DoHwan Park
Affiliation: University of Maryland, Department of Mathematics & Statistics
Title: Empirical Null Using Mixture Distributions and Its Application in Local False Discovery Rate
Abstract: When high dimensional data is given, it is often of interest to distinguish between significant (non-null, Ha) and non-significant (null, H0) group from mixture of two by controlling type I error rate. One popular way to control the level is the false discovery rate (FDR). This talk considers a method based on the local false discovery rate. In most of the previous studies, the null group is commonly assumed to be a normal distribution. However, if the null distribution can be departure from normal, there may exist too many or too few false discoveries (belongs null but rejected from the test) leading to the failure of controlling the given level of FDR. We propose a novel approach which enriches a class of null distribution based on mixture distributions. We provide real examples of gene expression data, fMRI data and protein domain data to illustrate the problems for overview.