A Fast Score Test for Generalized Mixture Models

• Activity Number: 72 - Semiparametric Modeling
• Type: Contributed
• Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Rui Duan* and Yang Ning and Shuang Wang and Bruce Lindsay and Raymond J. Carroll and Yong Chen
• Companies: University of Pennsylvania and Cornell University and Columbia University and Pennsylvania State University and Texas A & M University and University of Pennsylvania
• Keywords: Conditional likelihood; DNA methylation; Non-regular problem; Semiparametric mixture model

Abstract

In biomedical studies, testing for homogeneity between two groups, where one group is modeled by mixture models, is often of great interest. This paper considers the semiparametric exponential family mixture model proposed by Hong et al. (2017), and studies the score test for homogeneity under this model. The score test is nonregular in the sense that nuisance parameters disappear under the null hypothesis. To address this difficulty, we propose a modification of the score test, so that the resulting test enjoys the Wilks phenomenon. In finite samples, we show that with fixed nuisance parameters the score test is locally most powerful. In large samples, we establish the asymptotic power functions under two types of local alternative hypotheses. Our simulation studies illustrate that the proposed score test is powerful and computationally fast. We apply the proposed score test to an UK ovarian cancer DNA methylation data for identification of differentially methylated CpG sites.