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Lloyd-Jones, L.R., Nguyen, H.D. and McLachlan, G.J. (2018) A Globally Convergent Algorithm for Lasso-Penalized Mixture of Linear Regression Models. Computational Statistics and Data Analysis, 119, 19-38.
https://doi.org/10.1016/j.csda.2017.09.003

has been cited by the following article:

• TITLE: Adaptive Sparse Group Variable Selection for a Robust Mixture Regression Model Based on Laplace Distribution

  AUTHORS: Jiangtao Wang, Wanzhou Ye

  KEYWORDS: Robust Mixture Regression, Laplace Distribution, Adaptive Sparse Group Lasso

  JOURNAL NAME: Advances in Pure Mathematics, Vol.10 No.1, January 19, 2020

  ABSTRACT: The traditional estimation of Gaussian mixture model is sensitive to heavy-tailed errors; thus we propose a robust mixture regression model by assuming that the error terms follow a Laplace distribution in this article. And for the variable selection problem in our new robust mixture regression model, we introduce the adaptive sparse group Lasso penalty to achieve sparsity at both the group-level and within-group-level. As numerical experiments show, compared with other alternative methods, our method has better performances in variable selection and parameter estimation. Finally, we apply our proposed method to analyze NBA salary data during the period from 2018 to 2019.