Maximum likelihood methods in a robust censored errors-in-variables model

DOI10.1007/s11749-015-0439-1zbMath1341.62275OpenAlexW2015083885MaRDI QIDQ905111

Gustavo H. M. A. Rocha, Rosangela H. Loschi, Reinaldo B. Arellano-Valle

Publication date: 14 January 2016

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11749-015-0439-1

zbMATH Keywords

censored regressionexpectation-maximization algorithmscale mixtures of normal distributionsmultivariate \(t\) distribution

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Point estimation (62F10) Censored data models (62N01)

Related Items (6)

On a new mixture-based regression model: simulation and application to data with high censoring ⋮ On a tobit–Birnbaum–Saunders model with an application to medical data ⋮ Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads ⋮ Bayesian mismeasurementt-models for censored responses ⋮ Sparse estimation in high-dimensional linear errors-in-variables regression via a covariate relaxation method ⋮ A non-iterative Bayesian sampling algorithm for censored Student-tlinear regression models

Uses Software

• Stata
• sn
• tlmec

Cites Work

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• On some characterizations of the \(t\)-distribution
• Skew normal measurement error models
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• Elliptical structural models
• Maximum likelihood estimation via the ECM algorithm: A general framework
• The Importance of Assessing Measurement Reliability in Multivariate Regression
• Robustness of the student t based M-estimator
• A Heckman Selection-tModel
• The Skew-normal Distribution and Related Multivariate Families*

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