A Bayesian approach to disease clustering using restricted Chinese restaurant processes

DOI10.1214/20-EJS1696zbMath1439.62231OpenAlexW3016151185MaRDI QIDQ2180068

Abel Rodríguez, Claudia Wehrhahn, Tatiana Xifara, Samuel Leonard

Publication date: 13 May 2020

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1586397681

zbMATH Keywords

Chinese restaurant processareal datadisease clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

A Bayesian approach to disease clustering using restricted Chinese restaurant processes ⋮ Parsimonious Bayesian factor analysis for modelling latent structures in spectroscopy data

Uses Software

• CODA
• DCluster
• spBayes
• shallot

Cites Work

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• Defining predictive probability functions for species sampling models
• A probability for classification based on the Dirichlet process mixture model
• On species sampling sequences induced by residual allocation models
• Spatial product partition models
• On a nonparametric change point detection model in Markovian regimes
• Power comparisons for disease clustering tests
• Extension to the product partition model: computing the probability of a change
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator
• Ferguson distributions via Polya urn schemes
• Exchangeable and partially exchangeable random partitions
• A Bayesian approach to disease clustering using restricted Chinese restaurant processes
• A Bayesian analysis of some nonparametric problems
• Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels
• Clustering in linear-mixed models with a group fused lasso penalty
• Bayesian hierarchical models for detecting boundaries in areally referenced spatial datasets
• Classification method for disease risk mapping based on discrete hidden Markov random fields
• A test to detect clusters of disease
• A spatial scan statistic
• Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables
• Hidden Markov Models and Disease Mapping
• Bayesian Detection of Clusters and Discontinuities in Disease Maps
• Bayesian Detection and Modeling of Spatial Disease Clustering
• Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach

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