A Relaxation Approach to Feature Selection for Linear Mixed Effects Models-Reference-Cited by-同舟云学术

A Relaxation Approach to Feature Selection for Linear Mixed Effects Models

Published:2023-09-14 Issue: Volume: Page:1-15
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Sholokhov Aleksei¹,Burke James V.²,Santomauro Damian F.³⁴,Zheng Peng⁵,Aravkin Aleksandr⁶^ORCID

Affiliation:

1. Department of Applied Mathematics, University of Washington, Seattle, WA

2. Department of Mathematics, University of Washington, Seattle, WA

3. School of Public Health, The University of Queensland, Queensland Centre for Mental Health Research, Wacol, QLD, Australia

4. Institute of Health Metrics and Evaluation, University of Washington, Seattle, WA

5. Department of Health Metrics Sciences & Institute of Health Metrics and Evaluation, University of Washington, Seattle, WA

6. Department of Applied Mathematics & Institute of Health Metrics and Evaluation, University of Washington, Seattle, WA

Funder

Bill and Melinda Gates Foundation

U.S. NSF

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2023.2231496

Reference51 articles.

1. Aravkin, A., Burke, J., Sholokhov, A., and Zheng, P. (2022), “Analysis of Relaxation Methods for Feature Selection in Mixed Effects Models,” https://arxiv.org/abs/2205.06925.

2. Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods

3. Basis Pursuit Denoise With Nonsmooth Constraints

4. First-Order Methods in Optimization

5. Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models

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