Robust variable selection in finite mixture of regression models using the t distribution

DOI10.1080/03610926.2018.1513143OpenAlexW2898231092MaRDI QIDQ5077904

Junhui Yin, Zhengfen Xie, Lin Dai, Liu-Cang Wu

Publication date: 20 May 2022

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

Full work available at URL: https://doi.org/10.1080/03610926.2018.1513143

zbMATH Keywords

EM algorithmvariable selectionmixture modelLassoSCADt distributionhard

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Robustness and adaptive procedures (parametric inference) (62F35) Statistics (62-XX)

Related Items (1)

Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions

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