Simultaneous Parameter Estimation and Variable Selection via the LN-CASS Prior

TL;DR

This paper introduces the LN-CASS prior, a Bayesian approach that combines flexible parameter estimation with variable selection, applicable to classical models and effective in biological data analysis.

Contribution

The LN-CASS prior is a novel Bayesian prior that enables simultaneous parameter estimation and variable selection in interpretable models.

Findings

Performs comparably to machine learning methods in generalization.

Effective in metabolomics and genomics case studies.

Facilitates use of classical statistical models.

Abstract

We introduce a Bayesian prior distribution, the Logit-Normal continuous analogue of the spike-and-slab (LN-CASS), which enables flexible parameter estimation and variable/model selection in a variety of settings. We demonstrate its use and efficacy in three case studies -- a simulation study and two studies on real biological data from the fields of metabolomics and genomics. The prior allows the use of classical statistical models, which are easily interpretable and well-known to applied scientists, but performs comparably to common machine learning methods in terms of generalisability to previously unseen data.