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Activity Number: 407
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #314844 View Presentation
Title: PLEMT: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalized Exponential Tilt Mixture Models
Author(s): Chuan Hong* and Yong Chen and Yang Ning and Shuang Wang and Hao Wu and Raymond Carroll
Companies: The University of Texas School of Public Health and The University of Texas School of Public Health and Princeton University and Columbia University Mailman School of Public Health and Emory University and Texas A&M University
Keywords: Asymptotics ; Conditional likelihood ; Non-regular problem ; Penalized likelihood ; Semiparametric mixture model
Abstract:

Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely the generalized exponential tilt mixture model, to account for heterogeneity between differentially methylated and non-differentially methylated subjects in the cancer group, and capture the differences in higher order moments between subjects in cancer and normal groups. A pairwise pseudolikelihood is constructed to eliminate the unknown nuisance function. To circumvent boundary and non-identifiability problems as in parametric mixture models, we modify the pseudolikelihood by adding a penalty function. In addition, test with simple asymptotic distribution has computational advantages over permutational test for high-dimensional genetic and epigenetic data. We propose a pseudolikelihood based expectation--maximization test, and show the proposed test follows a simple chi-squared limiting distribution. Simulation studies show that the proposed test controls Type I errors well and has better power compared to several current tests. The proposed test is applied to a real data set to identify differentially methylated sites between ovarian cancer subjects and normal subjects.


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