Bayesian lasso Tobit regression

Other Title(s)

انحدار توبت لاسو البيزي

Time cited in Arcif : 
1

Author

al-Hilali, Haydar Kazim Abbas

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

Mathematics

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