Bayesian clustering of multiple zero-inflated outcomes
Beatrice Franzolini, Andrea Cremaschi, Willem van den Boom, Maria, De Iorio

TL;DR
This paper introduces a Bayesian nonparametric clustering method for multiple zero-inflated count processes, modeling zero-inflation and count distribution jointly to identify patterns and clusters in complex count data.
Contribution
It proposes a novel joint Bayesian model with a two-level clustering approach for multiple zero-inflated count processes, reducing parameters and capturing complex patterns.
Findings
Effective clustering of WhatsApp messaging data.
Flexible modeling of zero-inflation and count distribution.
Reduced parameter complexity compared to multivariate models.
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 nonparametric 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 to traditional multivariate approaches. The…
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Taxonomy
TopicsBayesian Methods and Mixture Models · Stochastic processes and statistical mechanics · Statistical Methods and Bayesian Inference
