Posterior property of Student-\(t\) linear regression model using objective priors

DOI10.1016/j.spl.2016.02.003zbMath1384.62108OpenAlexW2286222764MaRDI QIDQ274155

Min Wang, Mingan Yang

Publication date: 22 April 2016

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2016.02.003

zbMATH Keywords

Bayesian inferenceobjective priorsrobustness to outliers

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (3)

A simulation study to compare reference and other priors in the case of a standard univariate Student t-distribution ⋮ Objective Bayesian analysis for the Student-\(t\) linear regression ⋮ Laplace approximation and natural gradient for Gaussian process regression with heteroscedastic Student-\(t\) model

Cites Work

• A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model
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• State space mixed models for binary responses with scale mixture of normal distributions links
• Objective Bayesian analysis for the Student-t regression model
• Noninformative priors for one parameter of many
• Reference priors with partial information
• Multivariate Student-t regression models: Pitfalls and inference
• Bayesian analysis of elapsed times in continuous‐time Markov chains
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