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- W2560777746 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." @default.
- W2560777746 created "2016-12-16" @default.
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- W2560777746 date "2016-12-01" @default.
- W2560777746 modified "2023-10-13" @default.
- W2560777746 title "Bayesian inference on quasi-sparse count data" @default.
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- W2560777746 doi "https://doi.org/10.1093/biomet/asw053" @default.
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