Math @ Duke
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Publications [#327030] of David B. Dunson
search arxiv.org.Papers Published
- Datta, J; Dunson, DB, Bayesian inference on quasi-sparse count data.,
Biometrika, vol. 103 no. 4
(December, 2016),
pp. 971-983 [doi]
(last updated on 2025/04/11)
Abstract: 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.
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