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Wilcoxon-type multivariate cluster elastic net
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DOI：10.1016/j.neucom.2025.129358.png)
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摘要
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We propose a method for high dimensional multivariate regression that is robust to heavy-tailed distributions or outliers, while preserving estimation accuracy in normal random error distributions. We extend the Wilcoxontype regression to a multivariate regression model as a tuning-free approach to robustness. Furthermore, the proposed method regularizes the L1 and L2 terms of the clustering based on k-means, which is extended from the multivariate cluster elastic net. The estimation of the regression coefficient and variable selection are produced simultaneously. Moreover, considering the relationship among the correlation of response variables through the clustering is expected to improve the estimation performance. The numerical simulation demonstrates that our proposed method overperforms the multivariate cluster method and other multiple regression methods in the case of heavy-tailed error distribution and outliers. The proposed method also indicates stability in normal error distribution. Finally, we confirm the efficacy of our proposed method using gene data.
Keyword:
MM algorithm
Multivariate regression
Robust statistics
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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