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Simultaneous Parameter Estimation and Variable Selection via the LN-CASS Prior
- Publication Year :
- 2018
-
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.
- Subjects :
- Statistics - Applications
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1810.01692
- Document Type :
- Working Paper