Gaussian Scale Mixture Models for Robust Linear Multivariate Regression with Missing Data

DOI10.1080/03610918.2013.875565zbMath1360.62016OpenAlexW1987237065MaRDI QIDQ3178490

Juha Ala-Luhtala, Robert Piché

Publication date: 14 July 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2013.875565

zbMATH Keywords

missing datavariational BayesGaussian scale mixturerobust linear regression

Mathematics Subject Classification ID

Related Items (2)

On the robustness to outliers of the Student‐t process ⋮ On \(p\)-generalized elliptical random processes

Uses Software

• Matlab
• Octave
• PRMLT

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A method for simultaneous variable selection and outlier identification in linear regression
• Bayesian methods for neural networks and related models
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Explaining Variational Approximations
• The EM Algorithm and Extensions, 2E
• Robust Estimation of the Mean and Covariance Matrix from Data with Missing Values
• Parameter expansion to accelerate EM: the PX-EM algorithm
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
• Variational Bayesian Inference for a Nonlinear Forward Model
• Multivariate T-Distributions and Their Applications
• A Bayesian Formulation of Exploratory Data Analysis and Goodness‐of‐fit Testing*
• Variational Bayes for generalized autoregressive models
• Robust Estimation of a Location Parameter

This page was built for publication: Gaussian Scale Mixture Models for Robust Linear Multivariate Regression with Missing Data