Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior
File(s)
Published version
Author(s)
Thomson, W
Jabbari, S
Taylor, AE
Arlt, W
Smith, DJ
Type
Journal Article
Abstract
We introduce a Bayesian prior distribution, the logit-normal continuous analogue of the spike-and-slab, 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 generalizability to previously unseen data.
Date Issued
2019-01-02
Date Acceptance
2018-11-26
Citation
Journal of the Royal Society Interface, 2019, 16 (150)
ISSN
1742-5689
Publisher
The Royal Society
Journal / Book Title
Journal of the Royal Society Interface
Volume
16
Issue
150
Copyright Statement
© 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/30958174
Subjects
Bayesian
FALSE DISCOVERY RATE
Multidisciplinary Sciences
Science & Technology
Science & Technology - Other Topics
shrinkage
SHRINKAGE
spike-and-slab
variable selection
Publication Status
Published
Coverage Spatial
England
Article Number
20180572
Date Publish Online
2019-01-02