Franzolini, B;
Cremaschi, A;
Van Den Boom, W;
De Iorio, M;
(2023)
Bayesian clustering of multiple zero-inflated outcomes.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
, 381
(2247)
, Article 20220145. 10.1098/rsta.2022.0145.
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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 enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
| Type: | Article |
|---|---|
| Title: | Bayesian clustering of multiple zero-inflated outcomes |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1098/rsta.2022.0145 |
| Publisher version: | https://doi.org/10.1098/rsta.2022.0145 |
| Language: | English |
| Additional information: | © 2023 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
| Keywords: | conditional algorithm, enriched priors, excess-of-zeros data, finite mixtures, hurdle model, nested clustering |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10168385 |
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