Sparse estimation in high-dimensional linear errors-in-variables regression via a covariate relaxation method-Reference-Cited by-同舟云学术

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Sparse estimation in high-dimensional linear errors-in-variables regression via a covariate relaxation method

Published:2023-10-19 Issue:1 Volume:34 Page:
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Li Xin,Wu Dongya

Funder

Natural Science Foundation of Shaanxi Province of China

Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11222-023-10312-5.pdf

Reference35 articles.

1. Agarwal, A., Negahban, S., Wainwright, M.J.: Fast global convergence of gradient methods for high-dimensional statistical recovery. Ann Statist 40(5), 2452–2482 (2012)

2. Agarwal, A., Negahban, S.N., Wainwright, M.J.: Supplementary material: fast global convergence of gradient methods for high-dimensional statistical recovery. Ann. Stat. (2012b)

3. Belloni, A., Rosenbaum, M., Tsybakov, A.B.: An $$\ell _1, \ell _2, \ell _\infty $$-regularization approach to high-dimensional errors-in-variables models. Electron. J. Stat. 10(2), 1729–1750 (2016)

4. Belloni, A., Rosenbaum, M., Tsybakov, A.B.: Linear and conic programming estimators in high dimensional errors-in-variables models. J. R. Stat. Soc. Ser. B Stat. Methodol. 79(3), 939–956 (2017)

5. Bickel, P.J., Ritov, Y., Tsybakov, A.B.: Simultaneous analysis of Lasso and Dantzig selector. Ann. Stat. 37(4), 1705–1732 (2009)

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