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Activity Number: 245 - Methods for Analysis of High-Dimensional Data
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: SSC
Abstract #330480 Presentation
Title: Homogeneity Test Under Finite Mixture with Multi-Dimensional Parameter Kernel
Author(s): Ho Yin Ho* and Jiahua Chen
Companies: University of British Columbia and University of British Columbia
Keywords: finite mixture; test of homogeneity ; profile likelihood ratio; EM test
Abstract:

Due to the irregularity features of finite mixture model, classic inference procedures are usually not directly applicable. Many testing procedures have been studied for testing for homogeneity though only a few of them are concerning mixture with multi-dimensional parameter kernel density. We develop a framework for testing homogeneity based on the profile Likelihood ratio. The proposed test statistic has a nice and simple asymptotic distribution, mixture of Chi squares. Penalties on mixing proportion and model parameters are introduced to control type I error while preserving the power. The result is applicable for mixture models with general multi-dimensional parameter kernel densities. Specifically, mixture of Gamma distribution and mixture of Logistic distribution are studied in detail. Simulations are conducted to assess the size and power of the test.


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