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Trimmed LASSO regression estimator for binary response data

Author

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  • Sun, Hongwei
  • Cui, Yuehua
  • Gao, Qian
  • Wang, Tong

Abstract

A robust LASSO-type penalized logistic regression based on maximum trimmed likelihood is proposed. The robustness property of the proposed method is stated and proved. A comparison of the performances of the proposed method with regular LASSO was conducted via simulations.

Suggested Citation

  • Sun, Hongwei & Cui, Yuehua & Gao, Qian & Wang, Tong, 2020. "Trimmed LASSO regression estimator for binary response data," Statistics & Probability Letters, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:stapro:v:159:y:2020:i:c:s0167715219303256
    DOI: 10.1016/j.spl.2019.108679
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    References listed on IDEAS

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    1. Hadi, Ali S. & Luceno, Alberto, 1997. "Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 25(3), pages 251-272, August.
    2. Croux, Christophe & Flandre, Cécile & Haesbroeck, Gentiane, 2002. "The breakdown behavior of the maximum likelihood estimator in the logistic regression model," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 377-386, December.
    3. N. Neykov & P. Filzmoser & P. Neytchev, 2014. "Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator," Statistical Papers, Springer, vol. 55(1), pages 187-207, February.
    4. N. Neykov & P. Filzmoser & P. Neytchev, 2014. "Erratum to: Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator," Statistical Papers, Springer, vol. 55(3), pages 917-918, August.
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    Cited by:

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