High-Dimensional Cost-constrained Regression Via Nonconvex Optimization-Reference-Cited by-同舟云学术

High-Dimensional Cost-constrained Regression Via Nonconvex Optimization

Published:2021-05-04 Issue:1 Volume:64 Page:52-64
ISSN:0040-1706
Container-title:Technometrics
language:en
Short-container-title:Technometrics

Author:

Yu Guan¹,Fu Haoda²,Liu Yufeng³^ORCID

Affiliation:

1. Department of Biostatistics, State University of New York at Buffalo, NY;

2. Advanced Analytics and Data Sciences, Eli Lilly and Company, Indianapolis, IN;

3. Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC

Publisher

Informa UK Limited

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00401706.2021.1905071

Reference32 articles.

1. Gradient descent with non-convex constraints: local concavity determines convergence

2. Best subset selection via a modern optimization lens

3. An overview of kidney stone imaging techniques

4. A long-term comparison of pioglitazone and gliclazide in patients with Type 2 diabetes mellitus: a randomized, double-blind, parallel-group comparison trial

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