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

Adaptive Sparse Group Variable Selection for a Robust Mixture Regression Model Based on Laplace Distribution
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摘要 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. 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.
出处 《Advances in Pure Mathematics》 2020年第1期39-55,共17页 理论数学进展(英文)
关键词 ROBUST MIXTURE Regression LAPLACE Distribution ADAPTIVE SPARSE GROUP Lasso Robust Mixture Regression Laplace Distribution Adaptive Sparse Group Lasso
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