ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2023
Bayesian clustering of multiple zero-inflated outcomes.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.The annals of applied statistics · 2025Article
- Enriched Pitman-Yor processes.Scandinavian journal of statistics, theory and applications · 2025Article
- Product Centred Dirichlet Processes for Bayesian Multiview Clustering.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- A special issue on Bayesian inference: challenges, perspectives and prospects.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
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Authors and funding
4 authors.
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Abstract
Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an
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