A Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models-Reference-Cited by-同舟云学术

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A Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models

Published:2015-08-14 Issue:3 Volume:57 Page:325-345
ISSN:1369-1473
Container-title:Australian & New Zealand Journal of Statistics
language:en
Short-container-title:Aust. N. Z. J. Stat.

Author:

Kang Su Yun¹²,McGree James¹²,Baade Peter³⁴⁵,Mengersen Kerrie¹²

Affiliation:

1. Mathematical Sciences School; Queensland University of Technology; GPO Box 2434 Brisbane QLD 4001 Australia

2. CRC for Spatial Information; 204 Lygon Street Carlton Vic. 3053 Australia

3. Viertel Centre for Research in Cancer Control; Cancer Council Queensland; Gregory Terrace Fortitude Valley Australia

4. School of Public Health; Queensland University of Technology; GPO Box 2434 Brisbane QLD 4001 Australia

5. Griffith Health Institute; Griffith University; Brisbane QLD 4001 Australia

Funder

Australian Commonwealth's Cooperative Research Centres Programme

NHMRC Career Development Fellowship

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/anzs.12127/fullpdf

Reference42 articles.

1. Use of space-time models to investigate the stability of patterns of disease;Abellan;Environ. Health Persp.,2008

2. Statistical Inference

3. GeoDa: An introduction to spatial data analysis;Anselin;Geogr. Anal.,2006

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1. Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease;Journal of Geographical Systems;2022-02-19

2. A systematic review of Bayesian spatial–temporal models on cancer incidence and mortality;International Journal of Public Health;2020-05-24

3. Careful prior specification avoids incautious inference for log‐Gaussian Cox point processes;Journal of the Royal Statistical Society: Series C (Applied Statistics);2018-11-02

4. Spatiotemporal Suicide Risk in Germany: A Longitudinal Study 2007–11;Scientific Reports;2017-08-09

5. Sparse Approximate Inference for Spatio-Temporal Point Process Models;Journal of the American Statistical Association;2016-10-01

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