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Robust Bayesian Regularized Estimation Based on t Regression Model
Joint Authors
Source
Journal of Probability and Statistics
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-20
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The t distribution is a useful extension of the normal distribution, which can be used for statistical modeling of data sets with heavy tails, and provides robust estimation.
In this paper, in view of the advantages of Bayesian analysis, we propose a new robust coefficient estimation and variable selection method based on Bayesian adaptive Lasso t regression.
A Gibbs sampler is developed based on the Bayesian hierarchical model framework, where we treat the t distribution as a mixture of normal and gamma distributions and put different penalization parameters for different regression coefficients.
We also consider the Bayesian t regression with adaptive group Lasso and obtain the Gibbs sampler from the posterior distributions.
Both simulation studies and real data example show that our method performs well compared with other existing methods when the error distribution has heavy tails and/or outliers.
American Psychological Association (APA)
Li, Zean& Zhao, Weihua. 2015. Robust Bayesian Regularized Estimation Based on t Regression Model. Journal of Probability and Statistics،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1070012
Modern Language Association (MLA)
Li, Zean& Zhao, Weihua. Robust Bayesian Regularized Estimation Based on t Regression Model. Journal of Probability and Statistics No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1070012
American Medical Association (AMA)
Li, Zean& Zhao, Weihua. Robust Bayesian Regularized Estimation Based on t Regression Model. Journal of Probability and Statistics. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1070012
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1070012