Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior. Issue 150 (31st January 2019)

• Record Type: Journal Article
• Authors: Thomson, W.; Jabbari, S.; Taylor, A. E.; Arlt, W.; Smith, D. J.
• Is Part Of: Journal of the Royal Society interface. Volume 16:Issue 150(2019)
• Publication Date: 2019-01-31
• Subjects: Bayesian -- shrinkage -- spike-and-slab -- variable selection; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2018.0572
• Languages: English
• ISSNs: 1742-5689

Abstract

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.