- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Robust Quantile Horseshoe algorithm for sparse data | ||
| Al-Qadisiyah Journal of Pure Science | ||
| Article 3, Volume 31, Issue 1, July 2026, Pages 53-69 PDF (662 K) | ||
| Document Type: Mathematics | ||
| DOI: 10.29350/qjps.2026.170814.1070 | ||
| Authors | ||
| Ahmed Alhamzawi* 1; Gorgees Shaheed Mohammad2; Daniele Ettore Otera3 | ||
| 1Department of Mathematics, College of Science, University of AL-Qadisiyah, Iraq | ||
| 2Department of Mathematics, College of Education, University of AL-Qadisiyah, Iraq | ||
| 3Institute of Data Science and Digital Technologies, Faculty of Mathematics and Informatics, Vilnius University, 08412 Vilnius, Lithuania | ||
| Abstract | ||
| Current Quantile approaches relay on the asymmetric laplace distribution, which usually requires the solving of large matrix equations at every step that beomes very complex as the data size grows. To fix this, a faster method for high-dimensional Bayesian quantile regression is introduced. This method works directly on the posterior distribution without extra latent variables. By using a Horseshoe prior as the proposal, this algorithm avoids costly matrix operations. This reduces the computational cost from cubic O(p^3 ) to linear O(np) complexity. Tests on real and simulation data show that this method is much faster and more efficient than existing techniques. | ||
| Keywords | ||
| Bayesian inference; Horseshoe prior; shrinkage priors; slice sampling | ||
|
Statistics Article View: 76 PDF Download: 97 |
||