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• W2249506894 abstract "In a variety of application areas, there is a growing interest in analyzing high dimensional sparse count data. For example, in cancer genomic studies it is of interest to model the counts of mutated alleles across the genome. Existing approaches for analyzing multivariate count data via Poisson log-linear hierarchical models cannot flexibly adapt to the level and nature of sparsity in the data. We develop a new class of local-global shrinkage priors tailored for sparse counts. Theoretical properties are assessed, including posterior concentration, super-efficiency in estimating the sampling density, and robustness in posterior mean. Simulation studies illustrate excellent small sample properties relative to competitors, and we apply the method to model rare mutation data from the Exome Aggregation Consortium project." @default.
• W2249506894 created "2016-06-24" @default.
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• W2249506894 creator A5081284665 @default.
• W2249506894 date "2015-10-14" @default.
• W2249506894 modified "2023-09-27" @default.
• W2249506894 title "Priors for High-Dimensional Sparse Poisson Means" @default.
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• W2249506894 hasPublicationYear "2015" @default.
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