Geostatistical analysis of binomial data: Generalised linear or transformed Gaussianmodelling?

Environmetrics

Volumn 24, Issue 3, 2013, Pages 158-171

  Stanton, Michelle C ^a   Diggle, Peter J ^b,c  

^a LIVERPOOL SCHOOL OF TROPICAL MEDICINE   (United Kingdom)

^b LANCASTER UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF LIVERPOOL   (United Kingdom)

Author keywords

Model based geostatistics; Prevalence surveys; Spatial variation; Trans Gaussian kriging

Indexed keywords

DISEASE PREVALENCE; EYE DISEASE; GAUSSIAN METHOD; GEOSTATISTICS; HEALTH SURVEY; KRIGING; LINEARITY; MARKOV CHAIN; NUMERICAL MODEL; SPATIAL VARIATION; TROPICAL REGION; VILLAGE;

LOA LOA;

EID: 84876472937     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.2205     Document Type: Article

References (29)

1. Besag J, York J, Mollié A. 1991. Bayesian image restoration, with two applications in spatial statistics (with discussion). Annals of the Institute of Statistical Mathematics 43: 1-59.
2. Box G, Cox D. 1964. An analysis of transformations. Journal of the Royal Statistical Society B 26: 211-252.
3. Breslow N, Clayton D. 1994. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88: 9-25.
4. Chilès JP, Delfiner P. 1999. Geostatistics. Wiley: New York.
5. Clements ACA, Lwambo NJS, Blair L, Nyandindi U, Kaatano G, Kinunghi S, Webster JP, Fenwick A, Brooker S. 2006. Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in tanzania. Tropical Medicine and International Health 11: 490-503.
6. Cressie N. 1993. Statistics for Spatial Data. Wiley-Interscience: New York.
7. Dempster A, Laird N, Rubin D. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39: 1-38.
8. Diggle P, Moyeed R, Tawn J. 1998. Model-based geostatistics. Applied Statistics 47: 299-350.
9. Diggle P, Ribeiro P. 2007. Model-based Geostatistics. Springer: New York.
10. Diggle P, Thomson M, Christensen O, Rowlingson B, Obsomer V, Gardon J, Wanji S, Takougang I, Enyong P, Kamgno J, Remme J, Boussinesq M, Molyneux D. 2007. Spatial modelling and the prediction of Loa loa risk: decision making under uncertainty. Annals of Tropical Medicine and Parasitology 101: 1-11.
11. Eidsvik J, Martino S, Rue H. 2009. Approximate Bayesian inference in spatial generalized linear mixed models. Scandinavian Journal of Statistics 36: 1-22.
12. Elliot P, Cuzick J, English D, Stern R. 1996. Geographical and Environmental Epidemiology: Method for Small Area Studies. Oxford University Press: Oxford.
13. Gilks W, Richardson S, Spiegelhalter D. 1996. Markov Chain Monte Carlo in Practise. CRC Press: London.
14. Goovaerts P. 1994. Geostatistics for Natural Resource Evaluation. Oxford University Press: Oxford.
15. Harville DA. 1977. Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Society 72(358): 320-338.
16. Journel A. 1983. Nonparametric estimation of spatial distributions. Journal of the International Association for Mathematical Geology 9: 563-586.
17. Laird NM, Ware JH. 1982. Random-effects models for longitudinal data. Biometrics 38(4): 963-974.
18. Manly B. 1976. Exponential data transformations. Journal of the Royal Statistical Society Series D (The Statistician) 25: 37-42.
19. Matérn B. 1960. Spatial variation - stochastic models and their applications to some problems in forest surveys and other sampling investigations. Meddelanden fran Statens Skogsforskningsinstitut 49(5): 1-144.
20. Matérn B. 1978. Spatial Variation (Second Edition). Springer: Berlin.
21. Matheron G. 1976. A simple substitute for conditional expectation: the disjunctive kriging. In Advanced Geostatistics in the Mining Industry, Guarascio M, David M, Huijbregts C (eds). Reidel: Dordrecht; 231-236.
22. McCulloch CE. 1994. Maximum likelihood variance components estimation for binary data. Journal of the American Statistical Association 89: 330-335.
23. Moyeed R, Papritz A. 2002. An empirical comparison of kriging methods for non-linear spatial point prediction. Mathematical Geology 34: 365-386.
24. Papaspiliopoulus O, Roberts GO, Skold M. 2003. Non-centered parameterizations for hierarchical models and data augmentation. Bayesian statistics 7: 307-326.
25. Patil AP, Gething PW, Piel FB, Hay SI. 2011. Bayesian geostatistics in health cartography: the perspective of malaria. Trends in Parasitology 27: 246-253.
26. Rue H, Martino S, Chopin N. 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society Series B 71: 319-392.
27. Thomson M, Obsomer V, Kamgno J, Gardon J, Wanji S, Takougang I, Enyong P, Remme J, Molyneux D, Boussinesq M. 2004. Mapping the distribution of Loa loa in Cameroon in support of the African Programme for Onchocerciasis Control. Filaria Journal 3(7). doi:10.1186/1475-2883-3-7.
28. Utzinger J, Rinaldi L, Malone J, Krauth S, Kristensen T, Cringoli G, Bergquist R. 2011. Geospatial health: the first five years. Geospatial Health 6: 137-154.
29. WHO/APOC. 2010. The World Health Organization Year 2010 Progress Report. (available online at http://www.who.int/apoc/publications/APR_2010_EN_28Oct2010_Progress_report_final_printed_version.pdf).