Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Ali Alkenani"'
Autor:
Razaz Attar, Asmaa Almohanna, Ahlam Almusharraf, Amal Alhazmi, Nouf Alanzi, Fahad Al-Anezi, Turki Alanzi, Raghad Sroor, Ahmed Albishri, Amwaj Alzahrani, Taif Alsabilah, Ali Alkenani, Raghad Alghamdi, Fai AlGethami, Arub AlGethami
Publikováno v:
Frontiers in Medicine, Vol 10 (2023)
PurposeThe purpose of this study is to investigate the use of social media for the improvement of safety knowledge and awareness among phlebotomists.MethodsAs this study was intended to arrive at specific conclusions using empirical evidence, a deduc
Externí odkaz:
https://doaj.org/article/7785df5c7b3b49569ee327a50912ea4c
Autor:
Ali Alkenani, Basim Shlaibah Msallam
Publikováno v:
Journal of Probability and Statistics, Vol 2019 (2019)
Using the Pairwise Absolute Clustering and Sparsity (PACS) penalty, we proposed the regularized quantile regression QR method (QR-PACS). The PACS penalty achieves the elimination of insignificant predictors and the combination of predictors with indi
Externí odkaz:
https://doaj.org/article/60a11ace0ef84f418bb84d15c96042b4
Autor:
Ali Alkenani, Tahir R. Dikheel
Publikováno v:
Journal of Probability and Statistics, Vol 2017 (2017)
The elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients are the two issues raised in searching for the true model. Pairwise Absolute Clustering and Sparsity (PACS) achieves both goals. Unfortu
Externí odkaz:
https://doaj.org/article/9ed7bc3aaa724037a5eba2df452955e6
Autor:
Ali Alkenani
Publikováno v:
Gazi University Journal of Science. 35:748-763
The SSIR-PACS is a group identification and a model-free variable selection method under sufficient dimension reduction (SDR) settings. It combined the Pairwise Absolute Clustering and Sparsity (PACS) with sliced inverse regression (SIR) methods to p
Autor:
Dheyaa Alaboudi, Ali Alkenani
Publikováno v:
PROCEEDING OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED RESEARCH IN PURE AND APPLIED SCIENCE (ICARPAS2021): Third Annual Conference of Al-Muthanna University/College of Science.
Autor:
Ali Alkenani, Tawfik A. Saleh
Publikováno v:
Journal of Molecular Liquids. 367:120291
Autor:
E Rahman, Ali Alkenani
Publikováno v:
Journal of Physics: Conference Series. 1897:012018
In this article, we proposed a model-free variable selection method (SMAVE-EN). The concepts of sufficient dimension reduction (SDR) and regularization methods are combined to introduce SMAVE-EN. This method is proposed to produce a shrinkage estimat
Autor:
Ali Alkenani
Publikováno v:
Journal of Physics: Conference Series. 1664:012034
The shrinkage sliced inverse (SSIR) is a variable selection method under the settings of sufficient dimension reduction (SDR) theory. The SSIR merges the ideas of Lasso shrinkage and sliced inverse regression (SIR) to obtain sparse and accurate solut
Autor:
Ali Alkenani, Tahir R. Dikheel
Publikováno v:
Journal of Mathematics and Statistics. 12:192-200
The current paper proposes the sliced inverse quantile regression method (SIQR). In addition to the latter this study proposes both the sparse sliced inverse quantile regression method with Lasso (LSIQR) and Adaptive Lasso (ALSIQR) penalties. This ar
Autor:
Esraa Rahman, Ali Alkenani
Publikováno v:
Journal of Physics: Conference Series. 1591:012041
A new model-free variable selection method was proposed in this article, which is called SMAVE-AdEN. We combined the effective sufficient dimension reduction method MAVE with the variable selection method which is called adaptive Elastic Net (AdEN) t