Maximum likelihood estimation of the mixture of log-concave densities

DOI10.1016/j.csda.2016.03.002zbMath1466.62105OpenAlexW2293785346WikidataQ39866936 ScholiaQ39866936MaRDI QIDQ1659067

Weixin Yao, Hao Hu, Yichao Wu

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2016.03.002

zbMATH Keywords

consistencymixture modellog-concave maximum likelihood estimator (LCMLE)

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (8)

The robust EM-type algorithms for log-concave mixtures of regression models ⋮ Flexible families of symmetric and asymmetric distributions based on the two-piece skew normal distribution ⋮ An EM algorithm for fitting a mixture model with symmetric log-concave densities ⋮ Constraining kernel estimators in semiparametric copula mixture models ⋮ A Selective Overview and Comparison of Robust Mixture Regression Estimators ⋮ An overview of semiparametric extensions of finite mixture models ⋮ A semiparametric mixture method for local false discovery rate estimation from multiple studies ⋮ Semiparametric mixture regression with unspecified error distributions

Uses Software

• LogConcDEAD
• mixtools
• logcondens.mode

Cites Work

• Clustering with mixtures of log-concave distributions
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• The EM Algorithm and Extensions, 2E
• Detecting the Presence of Mixing with Multiscale Maximum Likelihood
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• Minimum profile Hellinger distance estimation for a semiparametric mixture model
• Label switching and its solutions for frequentist mixture models
• Bayesian Mixture Labeling by Highest Posterior Density
• Semiparametric location mixtures with distinct components
• Semiparametric Mixtures of Symmetric Distributions
• Semiparametric Estimation of a Two‐component Mixture Model where One Component is known
• Computing maximum likelihood estimators of a log-concave density function
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