The robust EM-type algorithms for log-concave mixtures of regression models

DOI10.1016/j.csda.2017.01.004zbMath1464.62092OpenAlexW2584647014WikidataQ44875672 ScholiaQ44875672MaRDI QIDQ1654229

Yichao Wu, Hao Hu, Weixin Yao

Publication date: 7 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.2017.01.004

zbMATH Keywords

EM algorithmrobust regressionlog-concave maximum likelihood estimatormixture of regression model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (5)

Mixture of linear experts model for censored data: a novel approach with scale-mixture of normal distributions ⋮ Modal linear regression using log-concave distributions ⋮ A Selective Overview and Comparison of Robust Mixture Regression Estimators ⋮ An overview of semiparametric extensions of finite mixture models ⋮ Semiparametric mixtures of regressions with single-index for model based clustering

Uses Software

• LogConcDEAD
• mixtools
• logcondens.mode

Cites Work

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