Clustering the prevalence of pediatric chronic conditions in the United States using distributed computing

DOI10.1214/18-AOAS1173zbMath1405.62224MaRDI QIDQ1624814

Yuchen Zheng, Nicoleta Serban

Publication date: 16 November 2018

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1532743481

zbMATH Keywords

distributed computingspatial clusteringMedicaidpediatric chronic conditions

Mathematics Subject Classification ID

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

Uses Software

• spatial
• GMRFLib
• Julia

Cites Work

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• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Fitting Gaussian Markov Random Fields to Gaussian Fields
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Combining Incompatible Spatial Data
• Hidden Markov Models and Disease Mapping
• Gaussian Markov Random Fields
• Applied Spatial Statistics for Public Health Data
• Statistics for Spatial Data

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