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A penalized likelihood estimation for mixture regressions with skew-normal errors

Author

Listed:
  • Libin Jin

    (Shanghai Lixin University of Accounting and Finance)

  • Shuyan Chen

    (University of Science and Technology of China)

  • Xiaowen Dai

    (Shanghai Lixin University of Accounting and Finance)

  • Lei Shi

    (Shanghai Lixin University of Accounting and Finance
    Yunnan University of Finance and Economics)

Abstract

This paper establishes identifiability results for mixture regression models with skew-normal errors under both fixed and random designs. We propose a novel penalized maximum likelihood estimation method for such models and demonstrate the strong consistency of the proposed estimator. An EM-type algorithm is developed to derive the penalized estimator. The finite sample properties of the proposed methodology are examined using extensive simulations, and a real data example is presented for illustration.

Suggested Citation

  • Libin Jin & Shuyan Chen & Xiaowen Dai & Lei Shi, 2025. "A penalized likelihood estimation for mixture regressions with skew-normal errors," Statistical Papers, Springer, vol. 66(6), pages 1-21, October.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:6:d:10.1007_s00362-025-01748-0
    DOI: 10.1007/s00362-025-01748-0
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    References listed on IDEAS

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