A multivariate linear regression analysis using finite mixtures of \(t\) distributions

DOI10.1016/j.csda.2013.01.017zbMath1471.62070OpenAlexW2038371718MaRDI QIDQ1621290

Giuliano Galimberti, Gabriele Soffritti

Publication date: 8 November 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.2013.01.017

zbMATH Keywords

EM algorithmmaximum likelihoodunobserved heterogeneitymodel identifiabilitynon-normal error distribution

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)

Related Items (18)

Bayesian linear regression models with flexible error distributions ⋮ Editorial: The 2nd special issue on advances in mixture models ⋮ Robust mixture regression modeling based on scale mixtures of skew-normal distributions ⋮ Robust fitting of mixture models using weighted complete estimating equations ⋮ Using mixtures in seemingly unrelated linear regression models with non-normal errors ⋮ The robust EM-type algorithms for log-concave mixtures of regression models ⋮ Two-layer EM algorithm for ALD mixture regression models: a new solution to composite quantile regression ⋮ Linear mixed models with marginally symmetric nonparametric random effects ⋮ Laplace mixture of linear experts ⋮ Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model ⋮ Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering ⋮ Finite mixture of regression models for censored data based on scale mixtures of normal distributions ⋮ Robust multivariate mixture regression models with incomplete data ⋮ Robust mixture regression modeling based on the normal mean-variance mixture distributions ⋮ Flexible regression modeling for censored data based on mixtures of Student-\(t\) distributions ⋮ Unnamed Item ⋮ Mixtures of multivariate contaminated normal regression models ⋮ Bayesian variable selection in linear regression models with non-normal errors

Uses Software

• mclust
• R
• UCI-ml
• flexmix
• Mixmod
• bootstrap

Cites Work

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