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of 93
pro vyhledávání: '"Wen, Canhong"'
Sparse reduced rank regression is an essential statistical learning method. In the contemporary literature, estimation is typically formulated as a nonconvex optimization that often yields to a local optimum in numerical computation. Yet, their theor
Externí odkaz:
http://arxiv.org/abs/2211.15889
The reduced-rank regression model is a popular model to deal with multivariate response and multiple predictors, and is widely used in biology, chemometrics, econometrics, engineering, and other fields. In the reduced-rank regression modelling, a cen
Externí odkaz:
http://arxiv.org/abs/2207.00924
Autor:
Tian, Ting, Luo, Wenxiang, Jiang, Yukang, Chen, Minqiong, Wen, Canhong, Pan, Wenliang, Wang, Xueqin
The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and societal importance to evaluate the effects of interventions in the pandemi
Externí odkaz:
http://arxiv.org/abs/2006.00523
K-SVD algorithm has been successfully applied to image denoising tasks dozens of years but the big bottleneck in speed and accuracy still needs attention to break. For the sparse coding stage in K-SVD, which involves $\ell_{0}$ constraint, prevailing
Externí odkaz:
http://arxiv.org/abs/2001.06780
We design a new algorithm on the best subset selection model in reduced rank regression.
Comment: This paper has been withdrawn by the authors due to a crucial error in the design of the algorithm
Comment: This paper has been withdrawn by the authors due to a crucial error in the design of the algorithm
Externí odkaz:
http://arxiv.org/abs/1912.06590
We introduce a new R package, BeSS, for solving the best subset selection problem in linear, logistic and Cox's proportional hazard (CoxPH) models. It utilizes a highly efficient active set algorithm based on primal and dual variables, and supports s
Externí odkaz:
http://arxiv.org/abs/1709.06254
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Dec . 117(52), 33117-33123.
Externí odkaz:
https://www.jstor.org/stable/27006536
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Akademický článek
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Publikováno v:
Journal of the American Statistical Association; Mar2024, Vol. 119 Issue 545, p701-714, 14p