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Activity Number: 116 - Recent Advances in Cure Rate Models for Long-Term Survivors
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #304358
Title: Testing for Homogeneity in Two-Component Mixture Models: a Bayesian Model Comparison Approach
Author(s): Gyuhyeong Goh* and Wei-Wen Hsu and David Todem
Companies: Kansas State University and Kansas State University and Michigan State University
Keywords: Bayesian model comparison; Bayesian nonparametrics; Cure-rate models; Gaussian process; Two-component mixture models; Zero-inflated count data

As an effective tool for expanding the application domain of statistical modeling, the two-component mixture approach has attracted great attention. In many applications, it is of interest to perform statistical testing for homogeneity in two-component mixture models. Many testing procedures for checking the constant mixing weights have been proposed for certain parametric models such as cure rate models and zero-inflated models. However, a general testing procedure for two-component mixture models is still limited. In this study, we develop a general framework to perform testing for homogeneity in two-component mixture models using a Bayesian model comparison approach. The proposed method is based on Bayesian nonparametric modeling with Gaussian processes that provides the flexibility necessary to handle a wide variety of data. The methodology is examined and exemplified through simulation study and real data analysis.

Authors who are presenting talks have a * after their name.

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