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The robust EM-type algorithms for log-concave mixtures of regression models

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  • Hu, Hao
  • Yao, Weixin
  • Wu, Yichao

Abstract

Finite mixture of regression (FMR) models can be reformulated as incomplete data problems and they can be estimated via the expectation–maximization (EM) algorithm. The main drawback is the strong parametric assumption such as FMR models with normal distributed residuals. The estimation might be biased if the model is misspecified. To relax the parametric assumption about the component error densities, a new method is proposed to estimate the mixture regression parameters by only assuming that the components have log-concave error densities but the specific parametric family is unknown.

Suggested Citation

  • Hu, Hao & Yao, Weixin & Wu, Yichao, 2017. "The robust EM-type algorithms for log-concave mixtures of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 14-26.
    • Handle: RePEc:eee:csdana:v:111:y:2017:i:c:p:14-26
    DOI: 10.1016/j.csda.2017.01.004
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    References listed on IDEAS

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    2. Mirfarah, Elham & Naderi, Mehrdad & Chen, Ding-Geng, 2021. "Mixture of linear experts model for censored data: A novel approach with scale-mixture of normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

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