An automatic robust Bayesian approach to principal component regression

DOI10.1080/02664763.2019.1710478OpenAlexW3001983193WikidataQ126330723 ScholiaQ126330723MaRDI QIDQ5861513

Philippe Gagnon, Alain Desgagné, Mylène Bédard

Publication date: 1 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.06341

zbMATH Keywords

outliersprincipal component analysisdimension reductionlinear regressionreversible jump algorithmswhole robustness

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35) Applications of statistics (62Pxx)

Related Items

Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics, Robust correlation scaled principal component regression, Informed reversible jump algorithms, Theoretical properties of Bayesian Student-\(t\) linear regression

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