Statistical inference for normal mixtures with unknown number of components

DOI10.1214/22-EJS2061OpenAlexW4312843820MaRDI QIDQ2084470

Mian Huang, Weixin Yao, Shiyi Tang

Publication date: 18 October 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-2/Statistical-inference-for-normal-mixtures-with-unknown-number-of-components/10.1214/22-EJS2061.full

zbMATH Keywords

EM algorithmorder selectionpenalized estimationnormal mixture model

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Nearly unbiased variable selection under minimax concave penalty
• Hypothesis testing for finite mixture models
• Sparse estimators and the oracle property, or the return of Hodges' estimator
• Convergence rates of parameter estimation for some weakly identifiable finite mixtures
• Hypothesis test for normal mixture models: the EM approach
• Note on the consistency of the maximum likelihood estimate for nonidentifiable distributions
• Asymptotics for likelihood ratio tests under loss of identifiability
• Testing the order of a model using locally conic parametrization: Population mixtures and stationary ARMA processes
• Likelihood ratio inequalities with applications to various mixtures
• Nonconcave penalized likelihood with a diverging number of parameters.
• Asymptotics for the likelihood ratio test in a two-component normal mixture model
• Optimal rate of convergence for finite mixture models
• Strong identifiability and optimal minimax rates for finite mixture estimation
• Estimating the number of components in finite mixture models via the group-sort-fuse procedure
• Likelihood inference in some finite mixture models
• Finite mixture and Markov switching models.
• Strong oracle optimality of folded concave penalized estimation
• Model Selection for Gaussian Mixture Models
• Variable Selection in Finite Mixture of Regression Models
• Penalized Maximum Likelihood Estimator for Normal Mixtures
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Dealing With Label Switching in Mixture Models
• Testing for a Finite Mixture Model with Two Components
• Detecting a Major Gene in an F2 Population
• Likelihood-Based Selection and Sharp Parameter Estimation
• Bayesian Mixture Labeling by Highest Posterior Density
• Order Selection in Finite Mixture Models With a Nonsmooth Penalty

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