Estimation of Linear Functionals in High-Dimensional Linear Models: From Sparsity to Nonsparsity-Reference-Cited by-同舟云学术

Estimation of Linear Functionals in High-Dimensional Linear Models: From Sparsity to Nonsparsity

Published:2023-06-22 Issue: Volume: Page:1-13
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Zhao Junlong¹^ORCID,Zhou Yang¹,Liu Yufeng²^ORCID

Affiliation:

1. School of Statistics, Beijing Normal University, Beijing, China

2. 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 at Chapel Hill, NC

Funder

Division of Mathematical Sciences

National Institute of General Medical Sciences

National Institutes of Health

National Natural Science Foundation of China

Yang Zhou’s research

Postdoctoral Science Foundation

Youth Fund

Yufeng Liu’s research

US NIH

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2023.2206084

Reference27 articles.

1. Estimation of linear projections of non-sparse coefficients in high-dimensional regression

2. Benign overfitting in linear regression

3. Least squares after model selection in high-dimensional sparse models

4. Simultaneous analysis of Lasso and Dantzig selector

5. Statistics for High-Dimensional Data

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