Bayesian adaptive Lasso Tobit regression
Other Title(s)
انحدار adaptive Lasso Tobit البيزي
Joint Authors
Zahir, Rahim Jabbar
al-Hilali, Haydar Kazim Abbas
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 11, Issue 1 (31 Mar. 2019), pp.1-10, 10 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2019-03-31
Country of Publication
Iraq
No. of Pages
10
Main Subjects
Abstract EN
In this paper, we introduce a new procedure for model selection in Tobit regression, we suggest the Bayesian adaptive Lasso Tobit regression (BALTR) for variable selection (VS) and coefficient estimation.
We submitted a Bayesian hierarchical model and Gibbs sampler (GS) for our procedure.
Our proposed procedure is clarified by means of simulations and a real data analysis.
Results demonstrate our procedure performs well in comparison to further procedures
American Psychological Association (APA)
al-Hilali, Haydar Kazim Abbas& Zahir, Rahim Jabbar. 2019. Bayesian adaptive Lasso Tobit regression. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 11, no. 1, pp.1-10.
https://search.emarefa.net/detail/BIM-971647
Modern Language Association (MLA)
al-Hilali, Haydar Kazim Abbas& Zahir, Rahim Jabbar. Bayesian adaptive Lasso Tobit regression. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 11, no. 1 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-971647
American Medical Association (AMA)
al-Hilali, Haydar Kazim Abbas& Zahir, Rahim Jabbar. Bayesian adaptive Lasso Tobit regression. al-Qadisiyah Journal for Computer Science and Mathematics. 2019. Vol. 11, no. 1, pp.1-10.
https://search.emarefa.net/detail/BIM-971647
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 9-10
Record ID
BIM-971647
