Robust Bayesian Regression with Synthetic Posterior Distributions.
Bayesian bootstrap
Bayesian lasso
Gibbs sampling
divergence
linear regression
Journal
Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874
Informations de publication
Date de publication:
15 Jun 2020
15 Jun 2020
Historique:
received:
29
04
2020
revised:
04
06
2020
accepted:
10
06
2020
entrez:
8
12
2020
pubmed:
9
12
2020
medline:
9
12
2020
Statut:
epublish
Résumé
Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not necessarily straightforward. We here propose a Bayesian approach to robust inference on linear regression models using synthetic posterior distributions based on
Identifiants
pubmed: 33286432
pii: e22060661
doi: 10.3390/e22060661
pmc: PMC7517196
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Japan Society for the Promotion of Science
ID : 17K14233
Organisme : Japan Society for the Promotion of Science
ID : 18K12757
Références
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pubmed: 27840585
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pubmed: 28493315
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pubmed: 31942084