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

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  • Xiudi Li
  • Ali Shojaie

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Suggested Citation

  • Xiudi Li & Ali Shojaie, 2020. "Discussion of “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1717-1719, December.
    • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:532:p:1717-1719
    DOI: 10.1080/01621459.2020.1837139
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    Cited by:

    1. Jack Jewson & David Rossell, 2022. "General Bayesian loss function selection and the use of improper models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1640-1665, November.
    2. Yuyang Liu & Pengfei Pi & Shan Luo, 2023. "A semi-parametric approach to feature selection in high-dimensional linear regression models," Computational Statistics, Springer, vol. 38(2), pages 979-1000, June.
    3. Mingyang Ren & Sanguo Zhang & Junhui Wang, 2023. "Consistent estimation of the number of communities via regularized network embedding," Biometrics, The International Biometric Society, vol. 79(3), pages 2404-2416, September.
    4. Canhong Wen & Zhenduo Li & Ruipeng Dong & Yijin Ni & Wenliang Pan, 2023. "Simultaneous Dimension Reduction and Variable Selection for Multinomial Logistic Regression," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1044-1060, September.

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