Bayesian lasso Tobit regression
Other Title(s)
انحدار توبت لاسو البيزي
Author
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 11, Issue 2 (30 Jun. 2019), pp.1-13, 13 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2019-06-30
Country of Publication
Iraq
No. of Pages
13
Main Subjects
Abstract EN
In the present research, we have proposed a new approach for model selection in Tobit regression.
The new technique uses Bayesian Lasso in Tobit regression (BLTR).
It has many features that give optimum estimation and variable selection property.
Specifically, we introduced a new hierarchal model.
Then, a new Gibbs sampler is introduced.
We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations.
A comparison was made with other previous techniques applying the simulation examples and real data.
It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.
American Psychological Association (APA)
al-Hilali, Haydar Kazim Abbas. 2019. Bayesian lasso Tobit regression. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 11, no. 2, pp.1-13.
https://search.emarefa.net/detail/BIM-883431
Modern Language Association (MLA)
al-Hilali, Haydar Kazim Abbas. Bayesian lasso Tobit regression. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 11, no. 2 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-883431
American Medical Association (AMA)
al-Hilali, Haydar Kazim Abbas. Bayesian lasso Tobit regression. al-Qadisiyah Journal for Computer Science and Mathematics. 2019. Vol. 11, no. 2, pp.1-13.
https://search.emarefa.net/detail/BIM-883431
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 10-12
Record ID
BIM-883431