Bayesian hierarchical robust factor analysis models for partially observed sample-selection data

DOI10.1016/j.jmva.2017.11.003zbMath1499.62200OpenAlexW2770655266MaRDI QIDQ1686243

Hea-Jung Kim

Publication date: 21 December 2017

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2017.11.003

zbMATH Keywords

Markov chain Monte Carlosample selectionfactor analysisbias correctionBayesian hierarchical modelscale mixtures of normalsmissing-not-at-random

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Bayesian inference (62F15)

Related Items (1)

Bayesian analysis of semiparametric Bernstein polynomial regression models for data with sample selection

Uses Software

• WinBUGS

Cites Work

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• A skew-normal factor model for the analysis of student satisfaction towards university courses
• A unified view on skewed distributions arising from selections
• Computing marginal likelihoods from a single MCMC output
• Multivariate measurement error models based on scale mixtures of the skew–normal distribution
• Skewed factor models using selection mechanisms

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