Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”

Lan Wang; Bo Peng; Jelena Bradic; Runze Li; Yunan Wu

doi:10.1080/01621459.2020.1843865

Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”

Lan Wang, Bo Peng, Jelena Bradic, Runze Li, Yunan Wu

Research output: Contribution to journal › Article › peer-review

Original language	English (US)
Pages (from-to)	1726-1729
Number of pages	4
Journal	Journal of the American Statistical Association
Volume	115
Issue number	532
DOIs	https://doi.org/10.1080/01621459.2020.1843865
State	Published - 2020

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1080/01621459.2020.1843865

Cite this

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title = "Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”",

author = "Lan Wang and Bo Peng and Jelena Bradic and Runze Li and Yunan Wu",

year = "2020",

doi = "10.1080/01621459.2020.1843865",

language = "English (US)",

volume = "115",

pages = "1726--1729",

journal = "Journal of the American Statistical Association",

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publisher = "Taylor and Francis Ltd.",

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TY - JOUR

T1 - Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”

AU - Wang, Lan

AU - Peng, Bo

AU - Bradic, Jelena

AU - Li, Runze

AU - Wu, Yunan

PY - 2020

Y1 - 2020

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DO - 10.1080/01621459.2020.1843865

M3 - Article

AN - SCOPUS:85097801607

SN - 0162-1459

VL - 115

SP - 1726

EP - 1729

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

IS - 532

ER -

Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”

All Science Journal Classification (ASJC) codes

Access to Document

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