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Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease-Mapping Models

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Cited by:

  1. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
  2. Marcos O. Prates & Douglas R. M. Azevedo & Ying C. MacNab & Michael R. Willig, 2022. "Non‐separable spatio‐temporal models via transformed multivariate Gaussian Markov random fields," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1116-1136, November.
  3. Garritt L. Page & Yajun Liu & Zhuoqiong He & Donchu Sun, 2017. "Estimation and Prediction in the Presence of Spatial Confounding for Spatial Linear Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 780-797, September.
  4. Widemberg S. Nobre & Alexandra M. Schmidt & João B. M. Pereira, 2021. "On the Effects of Spatial Confounding in Hierarchical Models," International Statistical Review, International Statistical Institute, vol. 89(2), pages 302-322, August.
  5. Jonathan R. Bradley & Christopher K. Wikle & Scott H. Holan, 2016. "Bayesian Spatial Change of Support for Count-Valued Survey Data With Application to the American Community Survey," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 472-487, April.
  6. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
  7. Brian J. Reich & Montserrat Fuentes & Amy H. Herring & Kelly R. Evenson, 2010. "Bayesian Variable Selection for Multivariate Spatially Varying Coefficient Regression," Biometrics, The International Biometric Society, vol. 66(3), pages 772-782, September.
  8. Joshua P. Keller & Adam A. Szpiro, 2020. "Selecting a scale for spatial confounding adjustment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1121-1143, June.
  9. MacTavish, Robert & Bixby, Honor & Cavanaugh, Alicia & Agyei-Mensah, Samuel & Bawah, Ayaga & Owusu, George & Ezzati, Majid & Arku, Raphael & Robinson, Brian & Schmidt, Alexandra M. & Baumgartner, Jill, 2023. "Identifying deprived “slum” neighbourhoods in the Greater Accra Metropolitan Area of Ghana using census and remote sensing data," World Development, Elsevier, vol. 167(C).
  10. Duncan Lee & Richard Mitchell, 2013. "Locally adaptive spatial smoothing using conditional auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 593-608, August.
  11. Jennifer F. Bobb & Maricela F. Cruz & Stephen J. Mooney & Adam Drewnowski & David Arterburn & Andrea J. Cook, 2022. "Accounting for spatial confounding in epidemiological studies with individual‐level exposures: An exposure‐penalized spline approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1271-1293, July.
  12. Duncan Lee & Alastair Rushworth & Sujit K. Sahu, 2014. "A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution," Biometrics, The International Biometric Society, vol. 70(2), pages 419-429, June.
  13. Hauke Thaden & Thomas Kneib, 2018. "Structural Equation Models for Dealing With Spatial Confounding," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 239-252, July.
  14. Janine B. Illian & David F. R. P. Burslem, 2017. "Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 495-520, October.
  15. Trevor J. Hefley & Mevin B. Hooten & Ephraim M. Hanks & Robin E. Russell & Daniel P. Walsh, 2017. "The Bayesian Group Lasso for Confounded Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 42-59, March.
  16. Thomas Kneib & Nadja Klein & Stefan Lang & Nikolaus Umlauf, 2019. "Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 1-39, March.
  17. Emiko Dupont & Simon N. Wood & Nicole H. Augustin, 2022. "Spatial+: A novel approach to spatial confounding," Biometrics, The International Biometric Society, vol. 78(4), pages 1279-1290, December.
  18. 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.
  19. Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
  20. Brian J. Reich & James S. Hodges, 2008. "Modeling Longitudinal Spatial Periodontal Data: A Spatially Adaptive Model with Tools for Specifying Priors and Checking Fit," Biometrics, The International Biometric Society, vol. 64(3), pages 790-799, September.
  21. Carlos García & Zaida Quiroz & Marcos Prates, 2023. "Bayesian spatial quantile modeling applied to the incidence of extreme poverty in Lima–Peru," Computational Statistics, Springer, vol. 38(2), pages 603-621, June.
  22. Alex Diana & Emily Beth Dennis & Eleni Matechou & Byron John Treharne Morgan, 2023. "Fast Bayesian inference for large occupancy datasets," Biometrics, The International Biometric Society, vol. 79(3), pages 2503-2515, September.
  23. Karim Anaya‐Izquierdo & Neal Alexander, 2021. "Spatial regression and spillover effects in cluster randomized trials with count outcomes," Biometrics, The International Biometric Society, vol. 77(2), pages 490-505, June.
  24. Staci A. Hepler & Robert J. Erhardt, 2021. "A spatiotemporal model for multivariate occupancy data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
  25. Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
  26. Donegan, Connor & Chun, Yongwan & Hughes, Amy E., 2020. "Bayesian estimation of spatial filters with Moran's eigenvectors and hierarchical shrinkage priors," OSF Preprints fah3z, Center for Open Science.
  27. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
  28. Philip A. White & Durban G. Keeler & Daniel Sheanshang & Summer Rupper, 2022. "Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
  29. Robert Hodges & Thomas Jagger & James Elsner, 2014. "The sun-hurricane connection: Diagnosing the solar impacts on hurricane frequency over the North Atlantic basin using a space–time model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 1063-1084, September.
  30. Isa Marques & Thomas Kneib & Nadja Klein, 2022. "Mitigating spatial confounding by explicitly correlating Gaussian random fields," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
  31. Michael L. Lavine & James S. Hodges, 2012. "On Rigorous Specification of ICAR Models," The American Statistician, Taylor & Francis Journals, vol. 66(1), pages 42-49, February.
  32. João B. M. Pereira & Widemberg S. Nobre & Igor F. L. Silva & Alexandra M. Schmidt, 2020. "Spatial confounding in hurdle multilevel beta models: the case of the Brazilian Mathematical Olympics for Public Schools," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1051-1073, June.
  33. Sandy Burden & Noel Cressie & David G. Steel, 2015. "The SAR Model for Very Large Datasets: A Reduced Rank Approach," Econometrics, MDPI, vol. 3(2), pages 1-22, May.
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