Bayesian inference on quasi-sparse count data

Jyotishka Datta and David B. Dunson

Biometrika, 2016, vol. 103, issue 4, 971-983

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.

Keywords: Count data; High-dimensional data; Local-global shrinkage; Rare variant; Shrinkage prior; Zero-inflation (search for similar items in EconPapers)
Date: 2016
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