# Testing-driven Variable Selection in Bayesian Modal Regression

arXiv: https://arxiv.org/abs/2510.23831 · HTML: https://mlsanity.com/paper/2510.23831/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO](https://mlsanity.com/paper/2010.06451/) (2020)
- [The modal age of Statistics](https://mlsanity.com/paper/1807.02789/) (2018)
- [Differential Network Analysis: A Statistical Perspective](https://mlsanity.com/paper/2003.04235/) (2020)
- [Semi-standard partial covariance variable selection when irrepresentable conditions fail](https://mlsanity.com/paper/1709.04840/) (2017)

## Abstract (arXiv)

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.