Advances in Pure Mathematics

Volume 10, Issue 1 (January 2020)

ISSN Print: 2160-0368   ISSN Online: 2160-0384

Google-based Impact Factor: 0.62  Citations  h5-index & Ranking

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

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DOI: 10.4236/apm.2020.101004    233 Downloads   507 Views  

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.

Cite this paper

Wang, J. and Ye, W. (2020) Adaptive Sparse Group Variable Selection for a Robust Mixture Regression Model Based on Laplace Distribution. Advances in Pure Mathematics, 10, 39-55. doi: 10.4236/apm.2020.101004.

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