Testing-driven Variable Selection in Bayesian Modal Regression

Jiasong Duan; Hongmei Zhang; Xianzheng Huang

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arXiv · 2510.23831

Testing-driven Variable Selection in Bayesian Modal Regression

Jiasong Duan·Hongmei Zhang·Xianzheng Huang

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Abstract

We propose a Bayesian variable selection method in the framework of modal regression for heavy-tailed responses. An efficient expectation-maximization algorithm is employed to expedite parameter estimation. A test statistic is constructed to exploit the shape of the model error distribution to effectively separate informative covariates from unimportant ones. Through simulations, we demonstrate and evaluate the efficacy of the proposed method in identifying important covariates in the presence of non-Gaussian model errors. Finally, we apply the proposed method to analyze two datasets arising in genetic and epigenetic studies.

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Jiasong Duan, Hongmei Zhang, Xianzheng Huang. 2025-10-27. Testing-driven Variable Selection in Bayesian Modal Regression. https://arxiv.org/abs/2510.23831

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