Empirical Bayes and Fully Bayes procedures to detect high-risk areas in disease mapping
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- Ying C. MacNab & Patrick J. Farrell & Paul Gustafson & Sijin Wen, 2004. "Estimation in Bayesian Disease Mapping," Biometrics, The International Biometric Society, vol. 60(4), pages 865-873, December.
- Ying C. MacNab, 2003. "Hierarchical Bayesian Modeling of Spatially Correlated Health Service Outcome and Utilization Rates," Biometrics, The International Biometric Society, vol. 59(2), pages 305-315, June.
- Dean, C. B. & Ugarte, M. D. & Militino, A. F., 2004. "Penalized quasi-likelihood with spatially correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 235-248, March.
- Peter Congdon, 2006. "A Model Framework for Mortality and Health Data Classified by Age, Area, and Time," Biometrics, The International Biometric Society, vol. 62(1), pages 269-278, March.
- C. B. Dean & M. D. Ugarte & A. F. Militino, 2001. "Detecting Interaction Between Random Region and Fixed Age Effects in Disease Mapping," Biometrics, The International Biometric Society, vol. 57(1), pages 197-202, March.
- Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
- Green P.J. & Richardson S., 2002. "Hidden Markov Models and Disease Mapping," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1055-1070, December.
- Ainsworth, L.M. & Dean, C.B., 2006. "Approximate inference for disease mapping," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2552-2570, June.
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- G. Vicente & T. Goicoa & P. Fernandez‐Rasines & M. D. Ugarte, 2020. "Crime against women in India: unveiling spatial patterns and temporal trends of dowry deaths in the districts of Uttar Pradesh," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 655-679, February.
- LeSage, James & Banerjee, Sudipto & Fischer, Manfred M. & Congdon, Peter, 2009. "Spatial statistics: Methods, models & computation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2781-2785, June.
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