Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.
Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr Lawson is Professor of Biostatistics in the Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, College of Medicine, MUSC and is an MUSC Distinguished Professor Emeritus and ASA Fellow. His PhD was in Spatial Statistics from the University of St. Andrews, UK. He has over 190 journal papers on the subject of spatial epidemiology, spatial statistics and related areas. In addition to a number of book chapters, he is the author of 10 books in areas related to spatial epidemiology and health surveillance. The most recent of these is Lawson, A.B. et al (eds) (2016) Handbook of Spatial Epidemiology. CRC Press, New York, and in 2018 a 3rd edition of Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology CRC Press. He has acted as an advisor in disease mapping and risk assessment for the World Health Organization (WHO) and is the founding editor of the Elsevier journal Spatial and Spatio-temporal Epidemiology. Dr Lawson has delivered many short courses in different locations over the last 20 years on Bayesian Disease Mapping with OpenBUGS, INLA, and Nimble, and more general spatial epidemiology topics. Web site: http://people.musc.edu/~abl6
Inhaltsangabe
1. Introduction and Data Sets 2. R Graphics and Spatial Health Data 3. Bayesian Hierarchical Models 4. Computation 5. Bayesian model Goodness of Fit Criteria 6. Bayesian Disease Mapping Models Part I Basic Software Approaches 7. BRugs/OpenBUGS 8. Nimble 9. CARBayes 10. INLA and R-INLA 11. Clustering, Latent Variable and Mixture Modeling 12. Spatio-Temporal Modeling with MCMC 13. Spatio-Temporal Modeling with INLA Part II Some Advanced and Special topics 14. Multivariate Models 15. Survival Modeling 16. Missingness, Measurement Error and Variable Selection 17. Individual Event Modeling 18. Infectious Disease Modeling
1. Introduction and Data Sets 2. R Graphics and Spatial Health Data 3. Bayesian Hierarchical Models 4. Computation 5. Bayesian model Goodness of Fit Criteria 6. Bayesian Disease Mapping Models
Part I Basic Software Approaches
7. BRugs/OpenBUGS 8. Nimble 9. CARBayes 10. INLA and R-INLA 11. Clustering, Latent Variable and Mixture Modeling 12. Spatio-Temporal Modeling with MCMC 13. Spatio-Temporal Modeling with INLA
1. Introduction and Data Sets 2. R Graphics and Spatial Health Data 3. Bayesian Hierarchical Models 4. Computation 5. Bayesian model Goodness of Fit Criteria 6. Bayesian Disease Mapping Models Part I Basic Software Approaches 7. BRugs/OpenBUGS 8. Nimble 9. CARBayes 10. INLA and R-INLA 11. Clustering, Latent Variable and Mixture Modeling 12. Spatio-Temporal Modeling with MCMC 13. Spatio-Temporal Modeling with INLA Part II Some Advanced and Special topics 14. Multivariate Models 15. Survival Modeling 16. Missingness, Measurement Error and Variable Selection 17. Individual Event Modeling 18. Infectious Disease Modeling
1. Introduction and Data Sets 2. R Graphics and Spatial Health Data 3. Bayesian Hierarchical Models 4. Computation 5. Bayesian model Goodness of Fit Criteria 6. Bayesian Disease Mapping Models
Part I Basic Software Approaches
7. BRugs/OpenBUGS 8. Nimble 9. CARBayes 10. INLA and R-INLA 11. Clustering, Latent Variable and Mixture Modeling 12. Spatio-Temporal Modeling with MCMC 13. Spatio-Temporal Modeling with INLA
