Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior
Research output: Contribution to journal › Article › peer-review
- Institute of Microbiology and Infection, College of Medical and Dental Science, University of Birmingham, Birmingham, UK.
- Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
- School of Mathematics, Watson Building; University of Birmingham; Edgbaston Birmingham B15 2TT UK
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.
|Journal||Journal of The Royal Society Interface|
|Early online date||2 Jan 2019|
|Publication status||Published - 31 Jan 2019|