Klaim Artikel Anda
Verifikasi kepemilikan artikel akademik
Apakah artikel-artikel ini milik Anda?
Daftarkan diri Anda sebagai author untuk mengklaim artikel dan dapatkan profil akademik terverifikasi dengan fitur lengkap.
Badge Verifikasi
Profil terverifikasi resmi
Statistik Lengkap
H-index, sitasi, dan metrik
Visibilitas Tinggi
Tampil di direktori author
Kelola Publikasi
Dashboard artikel terpadu
Langkah-langkah Klaim Artikel:
- 1. Daftar akun author dengan email akademik Anda
- 2. Verifikasi email dan lengkapi profil
- 3. Login dan buka menu "Klaim Artikel"
- 4. Cari dan klaim artikel Anda
- 5. Tunggu verifikasi dari admin (1-3 hari kerja)
Menampilkan 1–2 dari 2 artikel
Elastic Net Principal Component Regression With an Application
Afraa A. Hamada
International Journal of Science and Mathematics Education
Vol 1
, No 3
(2024)
To overcome the difficulties of high-dimensional data, Elastic Net Principal Component Regression (ENPCR), a potent statistical technique, combines Elastic Net regularization with Principal Component regression (PCR). When dealing with Multicollinearity among predictors, this method is especially helpful because it enables efficient variable selection while preserving interpretability. PCA is initially used in ENPCR to reduce the dataset's dimensionality by converting correlated variables into a...
Sumber Asli
Google Scholar
DOI
Two Stage Lasso in Principal Component Analysis With an Application
Afraa A. Hamada
International Journal of Applied Mathematics and Computing
Vol 1
, No 4
(2024)
This paper will employ a novel approach that builds upon the lasso method, utilizing it in two stages. The first stage applies to the principal components to select the important principal component and exclude the unimportant ones. This technique is effective in identifying significant principal components while attempting to eliminate bias in selecting these components over others. Additionally, it removes the ranking in determining the principal components compared to classical methods. Mo...
Sumber Asli
Google Scholar
DOI