Expectile trace regression via low-rank and group sparsity regularization-Reference-Cited by-同舟云学术

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Expectile trace regression via low-rank and group sparsity regularization

Published:2023-11-02 Issue:6 Volume:57 Page:1469-1489
ISSN:0233-1888
Container-title:Statistics
language:en
Short-container-title:Statistics

Author:

Peng Ling¹,Tan Xiangyong¹,Xiao Peiwen¹,Rizk Zeinab¹²,Liu Xiaohui¹

Affiliation:

1. School of Statistics and Data Science, and Key Laboratory of Data Science in Finance and Economics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, People's Republic of China

2. Faculty of Commerce, Department of Applied Mathematical Statistics, Damietta University, Damietta El-Gadeeda City, Damietta Governorate, Egypt

Funder

NSF of China

Jiangxi Provincial NSF

Science & Technology research project of the Education Department of Jiangxi Province

China Postdoctoral Science Foundation

National Social Science Foundation of China

Outstanding Youth Fund Project of the Science and Technology Department of Jiangxi Province

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02331888.2023.2269588

Reference34 articles.

1. Generalized high-dimensional trace regression via nuclear norm regularization

2. Macro Factors in Bond Risk Premia

3. Forecasting Using Principal Components From a Large Number of Predictors

4. Estimation of (near) low-rank matrices with noise and high-dimensional scaling

5. The Dantzig selector and sparsity oracle inequalities

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