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2020 A Bayesian approach to disease clustering using restricted Chinese restaurant processes
Claudia Wehrhahn, Samuel Leonard, Abel Rodriguez, Tatiana Xifara
Electron. J. Statist. 14(1): 1449-1478 (2020). DOI: 10.1214/20-EJS1696


Identifying disease clusters (areas with an unusually high incidence of a particular disease) is a common problem in epidemiology and public health. We describe a Bayesian nonparametric mixture model for disease clustering that constrains clusters to be made of adjacent areal units. This is achieved by modifying the exchangeable partition probability function associated with the Ewen’s sampling distribution. We call the resulting prior the Restricted Chinese Restaurant Process, as the associated full conditional distributions resemble those associated with the standard Chinese Restaurant Process. The model is illustrated using synthetic data sets and in an application to oral cancer mortality in Germany.


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Claudia Wehrhahn. Samuel Leonard. Abel Rodriguez. Tatiana Xifara. "A Bayesian approach to disease clustering using restricted Chinese restaurant processes." Electron. J. Statist. 14 (1) 1449 - 1478, 2020.


Received: 1 May 2019; Published: 2020
First available in Project Euclid: 9 April 2020

zbMATH: 07200234
MathSciNet: MR4082474
Digital Object Identifier: 10.1214/20-EJS1696

Keywords: Areal data , Chinese restaurant process , Disease clustering


Vol.14 • No. 1 • 2020
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