Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Zhengfen Jin"'
Publikováno v:
Engineering Science and Technology, an International Journal, Vol 55, Iss , Pp 101731- (2024)
In this paper, we study the high-dimensional varying coefficient partially linear model and proposes a variable parameter selection method combined with elastic network. The two-stage estimation method is adopted and the alternating direction multipl
Externí odkaz:
https://doaj.org/article/2082bb47bc8f4046b4fae93b81b54188
Publikováno v:
Symmetry, Vol 16, Iss 3, p 303 (2024)
The matrix nuclear norm minimization problem has been extensively researched in recent years due to its widespread applications in control design, signal and image restoration, machine learning, big data problems, and more. One popular model is nucle
Externí odkaz:
https://doaj.org/article/9deb7fec901f488291a3df162a34bc8d
Publikováno v:
Mathematics, Vol 11, Iss 19, p 4220 (2023)
This paper mainly studies the application of the linearized alternating direction method of multiplier (LADMM) and the accelerated symmetric linearized alternating direction method of multipliers (As-LADMM) for high dimensional partially linear model
Externí odkaz:
https://doaj.org/article/ea31113d6a3248bf9bdb034d669b6fac
Publikováno v:
Mathematics, Vol 11, Iss 12, p 2726 (2023)
In this paper, a partially linear model based on the fused lasso method is proposed to solve the problem of high correlation between adjacent variables, and then the idea of the two-stage estimation method is used to study the solution of this model.
Externí odkaz:
https://doaj.org/article/c8c3e4f1ad8c43fa98b8e1bf8a6c6857
Publikováno v:
Mathematics; Volume 11; Issue 12; Pages: 2726
In this paper, a partially linear model based on the fused lasso method is proposed to solve the problem of high correlation between adjacent variables, and then the idea of the two-stage estimation method is used to study the solution of this model.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::c7274eb944058eb43208d771c8bb4075
Publikováno v:
Statistics, Optimization and Information Computing, Vol 4, Iss 2, Pp 174-182 (2016)
In this paper, we propose a two-step proximal gradient algorithm to solve nuclear norm regularized least squares for the purpose of recovering low-rank data matrix from sampling of its entries. Each iteration generated by the proposed algorithm is a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ece325f69c3fc16512698bf13b3d319
http://www.iapress.org/index.php/soic/article/view/201
http://www.iapress.org/index.php/soic/article/view/201