Asymptotic analysis of singular likelihood ratio of normal mixture by Bayesian learning theory for testing homogeneity

DOI10.1080/03610926.2020.1849721OpenAlexW3108930490MaRDI QIDQ5095982

Natsuki Kariya, Sumio Watanabe

Publication date: 12 August 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2020.1849721

zbMATH Keywords

likelihood ratio testBayesian statisticsmixture modelsingular model

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Hypothesis test for normal mixture models: the EM approach
• Asymptotics for likelihood ratio tests under loss of identifiability
• A Modified Likelihood Ratio Test for Homogeneity in Finite Mixture Models
• Algebraic Analysis for Nonidentifiable Learning Machines
• Inference on the Order of a Normal Mixture
• Testing for a Finite Mixture Model with Two Components
• Learning Coefficients of Layered Models When the True Distribution Mismatches the Singularities
• Testing Homogeneity in a Mixture Distribution via theL²Distance Between Competing Models
• Likelihood ratio test for univariate Gaussian mixture

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