Modified adaptive group lasso for high-dimensional varying coefficient models-Reference-Cited by-同舟云学术

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Modified adaptive group lasso for high-dimensional varying coefficient models

Published:2020-08-14 Issue: Volume: Page:1-16
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Wang Mingqiu¹,Kang Xiaoning²^ORCID,Tian Guo-Liang³

Affiliation:

1. School of Statistics, Qufu Normal University, Qufu, Shandong, P. R. China

2. International Business College and Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian, Liaoning, P. R. China

3. Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, Guangdong, P. R. China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Ministry of Education of China

Publisher

Informa UK Limited

Subject

Modelling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2020.1804936

Reference39 articles.

1. Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

2. Structural identification and variable selection in high-dimensional varying-coefficient models

3. Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data

4. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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