Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior.
J R Soc Interface
; 16(150): 20180572, 2019 01 31.
Article
in En
| MEDLINE
| ID: mdl-30958174
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Computer Simulation
/
Genomics
/
Models, Biological
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
J R Soc Interface
Year:
2019
Document type:
Article
Affiliation country:
United kingdom