Mapping Cancer Risk in Southwestern Ontario with Changing Census Boundaries

Biometrics

Volumn 68, Issue 4, 2012, Pages 1228-1237

  Nguyen, P ^a   Brown, P E ^b   Stafford, J ^b  

^a CANCER CARE ONTARIO   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

Disease mapping; Kernel smoothing; Local likelihood; Local EM; Nonparametric inference; Spatial statistics

Indexed keywords

BIOLOGICAL ORGANS; DISEASES; UNCERTAINTY ANALYSIS;

CANCER RISK; DISEASE MAPPING; DISEASE RISKS; KERNEL SMOOTHING; LOCAL LIKELIHOOD; LOCAL-EM; NONPARAMETRIC INFERENCE; ONTARIO; SPATIAL STATISTICS; TIME-PERIODS;

MAPPING;

BIOACCUMULATION; BOOTSTRAPPING; CANCER; CENSUS; DATA SET; DISEASE PREVALENCE; MAPPING METHOD; RISK FACTOR; SPATIAL ANALYSIS;

CANADA; ONTARIO [CANADA];

EID: 84871653990     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2012.01792.x     Document Type: Article

References (17)

1. Besag, J., York, J., and Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43, 1-20.
2. Betensky, R. A., Lindsey, J. C., Ryan, L.M., & Wand, M. P. (2002). A local likelihood proportional hazards model for interval censored data. Statistics in Medicine 21, 263-275.
3. Brillinger, D. (1990). Spatial-temporal modeling of spatially aggregate birth data. Survey Methodology 16, 255-269.
4. Brillinger, D. (1991). Locally weighted analysis of spatially aggregate birth data: Uncertainty estimation and display. In Spatial Issues in Statistics, 91 volume of Proceedings of Statistics Canada Symposium, Ottawa. Statistics Canada.
5. Brillinger, D. (1994). Examples of scientific problems and data analyses in demography, neurophysiology, and seismology. Journal of Computational and Graphical Statistics 3, 1-22.
6. Fan, C.-P. S., Stafford, J., & Brown, P. E. (2011). Local-EM and the EMS algorithm. Journal of Computational and Graphical Statistics 20, 750-766.
7. Illian, J., Penttinen, A., Stoyan, H., & Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. Wiley Interscience.
8. Kelsall, J. and Wakefield, J. (2002). Modeling spatial variation in disease risk: A geostatistical approach. Journal of the American Statistical Association 97, 692-701.
9. Lawson, A. B. (2008). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. CRC Press.
10. Li, Y., Brown, P., Rue, H., al Maini, M., and Fortin, P. (2012). Spatial modelling of lupus incidence over 40 years with changes in census areas. Journal of the Royal Statistical Society. Series C (Applied Statistics) 61, 99-115.
11. Loader, C. (1999). Local Regression and Likelihood. Springer.
12. Loken, C., Gruner, D., Groer, L., Peltier, R., Bunn, N., Craig, M., Henriques, T., Dempsey, J., Yu, C.-H., Chen, J., Dursi, L. J., Chong, J., Northrup, S., Pinto, J., Knecht, N., and Zon, R. V. (2010). Scinet: Lessons learned from building a power-efficient top-20 system and data centre. Journal of Physics: Conference Series 256, 012026.
13. Murthy, S. S. and Testa, J. R. (1999). Asbestos, chromosomal deletions, and tumor suppressor gene alterations in human malignant mesothelioma. Journal of Cellular Physiology 180, 150-157.
14. (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. R Development Core Team Vienna, Austria: ISBN 3-900051-07-0.
15. Silverman, B., Jones, M., Wilson, J., and Nychka, D. (1990). A smoothed EM approach to indirect estimation problems, with particular, reference to stereology and emission tomography. Journal of the Royal Statistical Society. Series B (Methodological) 52, 271-324.
16. Tibshirani, R. and Hastie, T. (1987). Local likelihood estimation. Journal of the American Statistical Association 82, 559-567.
17. Yu, H. (2001). Rmpi: Interface (Wrapper) to MPI (Message-Passing Interface). R package version 0.6-0.