Rahim Alhamzawi
Biographic Data
| ID | 6835454 |
|---|---|
| NAME | Rahim Alhamzawi |
| GIVEN NAMES | Rahim |
| FAMILY NAME | Alhamzawi |
| SIGNATURE | ALHAMZAWI R |
| AFFILIATIONS | University of Al-Qadisiyah |
| ORCID | 0000-0003-1912-9579 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Bayesian group bridge composite quantile regression
Bayesian regularized composite quantile regression (CQR) method with group bridge penalty is adopted to conduct covariate selection and estimation in CQR. MCMC algorithm was improved for posterior inference employing a scale mixture of normal of the asymmetric Laplace distribution (ALD). The suggested algorithm uses priors for the coefficients of regression, which are scale mixtures of multivariate uniform distributions with a particular Gamma di…
Bayesian reciprocal bridge composite tobit quantile regression
A composite tobit quantile regression approach is proposed for Bayesian simultaneous covariate selection and estimation in the setting of left censored regression. The proposed approach uses prior distributions for the regression coefficients that are scale mixtures of inverse uniform priors on the coefficients and independent Gamma priors on their mixing parameters. The proposed method was illustrated using simulation examples. Results show that…
No prominent works on this page.
Bayesian reciprocal bridge composite tobit quantile regression
A composite tobit quantile regression approach is proposed for Bayesian simultaneous covariate selection and estimation in the setting of left censored regression. The proposed approach uses prior distributions for the regression coefficients that are scale mixtures of inverse uniform priors on the coefficients and independent Gamma priors on their mixing parameters. The proposed method was illustrated using simulation examples. Results show that…
Bayesian group bridge composite quantile regression
Bayesian regularized composite quantile regression (CQR) method with group bridge penalty is adopted to conduct covariate selection and estimation in CQR. MCMC algorithm was improved for posterior inference employing a scale mixture of normal of the asymmetric Laplace distribution (ALD). The suggested algorithm uses priors for the coefficients of regression, which are scale mixtures of multivariate uniform distributions with a particular Gamma di…
Advanced Statistical Methods and Models (2 works) · Bayesian inference (2 works) · Bayesian linear regression (2 works) · Bayesian Methods and Mixture Models (2 works) · Bayesian probability (2 works) · Covariate (2 works) · Mathematics (2 works) · Prior probability (2 works) · Quantile (2 works) · Quantile regression (2 works)