Spatial risk mapping for rare disease with hidden Markov fields and variational EM

DOI10.1214/13-AOAS629zbMath1288.62158arXiv1312.2800OpenAlexW3101399607MaRDI QIDQ2443178

Lamiae Azizi, Myriam Charras-Garrido, Florence Forbes, Senan Doyle, David Abrial

Publication date: 4 April 2014

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

Full work available at URL: https://arxiv.org/abs/1312.2800

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Markov processes: estimation; hidden Markov models (62M05) Medical applications (general) (92C50)

Related Items (3)

Spatial clustering of average risks and risk trends in Bayesian disease mapping ⋮ Spatial risk mapping for rare disease with hidden Markov fields and variational EM ⋮ A latent discrete Markov random field approach to identifying and classifying historical forest communities based on spatial multivariate tree species counts

Uses Software

• SaTScan

Cites Work

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• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Choosing initial values for the EM algorithm for finite mixtures
• Bayesian regularization for normal mixture estimation and model-based clustering
• Bayesian image restoration, with two applications in spatial statistics (with discussion)
• EM procedures using mean field-like approximations for Markov model-based image segmentation
• Spatial risk mapping for rare disease with hidden Markov fields and variational EM
• Disease Mapping of Stage-Specific Cancer Incidence Data
• A Hierarchical Model for Space–Time Surveillance Data on Meningococcal Disease Incidence
• A spatial scan statistic
• Hidden Markov Models and Disease Mapping
• Finite mixture models
• Bayesian Detection of Clusters and Discontinuities in Disease Maps
• Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach

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