Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior

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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.
Original languageEnglish
Article number20180572
Pages (from-to)20180572
JournalJournal of The Royal Society Interface
Volume16
Issue number150
Early online date2 Jan 2019
DOIs
Publication statusPublished - 31 Jan 2019

Keywords

  • Bayesian
  • Shrinkage
  • Spike-and-slab
  • Variable selection

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering

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