Bayesian inference on quasi-sparse count data.
Journal Article (Journal Article)
There is growing interest in analysing high-dimensional count data, which often exhibit quasi-sparsity corresponding to an overabundance of zeros and small nonzero counts. Existing methods for analysing multivariate count data via Poisson or negative binomial log-linear hierarchical models with zero-inflation cannot flexibly adapt to quasi-sparse settings. We develop a new class of continuous local-global shrinkage priors tailored to quasi-sparse counts. Theoretical properties are assessed, including flexible posterior concentration and stronger control of false discoveries in multiple testing. Simulation studies demonstrate excellent small-sample properties relative to competing methods. We use the method to detect rare mutational hotspots in exome sequencing data and to identify North American cities most impacted by terrorism.
Full Text
Duke Authors
Cited Authors
- Datta, J; Dunson, DB
Published Date
- December 2016
Published In
Volume / Issue
- 103 / 4
Start / End Page
- 971 - 983
PubMed ID
- 29422693
Pubmed Central ID
- PMC5793680
Electronic International Standard Serial Number (EISSN)
- 1464-3510
International Standard Serial Number (ISSN)
- 0006-3444
Digital Object Identifier (DOI)
- 10.1093/biomet/asw053
Language
- eng