Simultaneous Parameter Estimation and Variable Selection via the LN-CASS Prior
Applications
2018-10-04 v1
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.
Cite
@article{arxiv.1810.01692,
title = {Simultaneous Parameter Estimation and Variable Selection via the LN-CASS Prior},
author = {William Thomson and Sara Jabbari and Angela Taylor and Wiebke Arlt and David Smith},
journal= {arXiv preprint arXiv:1810.01692},
year = {2018}
}