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

Libin Jin, Shuyan Chen, Xiaowen Dai () and Lei Shi ()
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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

Statistical Papers, 2025, vol. 66, issue 6, No 4, 21 pages

Abstract: 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.

Keywords: Mixture regressions; Identifiability; Strong consistency; Penalized likelihood; VC class (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00362-025-01748-0

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