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Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love

Citations

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Cited by:

  1. 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.
  2. Georgia Papadogeorgou, 2022. "Discussion on “Spatial+: a novel approach to spatial confounding” by Emiko Dupont, Simon N. Wood, and Nicole H. Augustin," Biometrics, The International Biometric Society, vol. 78(4), pages 1305-1308, December.
  3. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
  4. Alex Reinhart & Joel Greenhouse, 2018. "Self‐exciting point processes with spatial covariates: modelling the dynamics of crime," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1305-1329, November.
  5. Walder, Adam & Hanks, Ephraim M., 2020. "Bayesian analysis of spatial generalized linear mixed models with Laplace moving average random fields," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  6. 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.
  7. 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.
  8. 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.
  9. Cici Bauer & Jon Wakefield, 2018. "Stratified space–time infectious disease modelling, with an application to hand, foot and mouth disease in China," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1379-1398, November.
  10. Brian J. Reich & Howard H. Chang & Kristen M. Foley, 2014. "A spectral method for spatial downscaling," Biometrics, The International Biometric Society, vol. 70(4), pages 932-942, December.
  11. 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.
  12. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
  13. 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.
  14. Libor Votava & Lenka Komárková & Jiří Dvořák, 2021. "Determinanty poptávky a nabídky na trhu s byty a jejich význam pro vysvětlení regionálních rozdílů [Demand and Supply Determinants on the Property Market and Their Importance in Explaining Regional," Politická ekonomie, Prague University of Economics and Business, vol. 2021(1), pages 26-47.
  15. 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.
  16. Joshua L. Warren & Melanie H. Chitwood & Benjamin Sobkowiak & Caroline Colijn & Ted Cohen, 2023. "Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data," Biometrics, The International Biometric Society, vol. 79(4), pages 3650-3663, December.
  17. 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.
  18. Emiko Dupont & Nicole H. Augustin, 2024. "Spatial Confounding and Spatial+ for Nonlinear Covariate Effects," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 455-470, September.
  19. Candace Berrett & William F. Christensen & Stephan R. Sain & Nathan Sandholtz & David W. Coats & Claudia Tebaldi & Hedibert F. Lopes, 2020. "Modeling sea‐level processes on the U.S. Atlantic Coast," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
  20. 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.
  21. 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.
  22. Zhao, Qing & Boomer, G. Scott & Silverman, Emily & Fleming, Kathy, 2017. "Accounting for the temporal variation of spatial effect improves inference and projection of population dynamics models," Ecological Modelling, Elsevier, vol. 360(C), pages 252-259.
  23. Erin M. Schliep & Alan E. Gelfand & James S. Clark & Roland Kays, 2018. "Joint Temporal Point Pattern Models for Proximate Species Occurrence in a Fixed Area Using Camera Trap Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(3), pages 334-357, September.
  24. Duncan Lee & Craig Anderson, 2023. "Delivering spatially comparable inference on the risks of multiple severities of respiratory disease from spatially misaligned disease count data," Biometrics, The International Biometric Society, vol. 79(3), pages 2691-2704, September.
  25. Steen MAGNUSSEN, 2018. "An estimation strategy to protect against over-estimating precision in a LiDAR-based prediction of a stand mean," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 64(12), pages 497-505.
  26. Lan Hu & Yongwan Chun & Daniel A. Griffith, 2020. "Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000–2010," Journal of Geographical Systems, Springer, vol. 22(3), pages 291-308, July.
  27. Xu Ning & Francis K. C. Hui & Alan H. Welsh, 2023. "A double fixed rank kriging approach to spatial regression models with covariate measurement error," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
  28. Chudzinska, Magda & Dupont, Yoko L. & Nabe-Nielsen, Jacob & Maia, Kate P. & Henriksen, Marie V. & Rasmussen, Claus & Kissling, W. Daniel & Hagen, Melanie & Trøjelsgaard, Kristian, 2020. "Combining the strengths of agent-based modelling and network statistics to understand animal movement and interactions with resources: example from within-patch foraging decisions of bumblebees," Ecological Modelling, Elsevier, vol. 430(C).
  29. Steinhubel, L., 2018. "Somewhere in between towns, markets, and neighbors Agricultural transition in the rural-urban interface of Bangalore, India," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277430, International Association of Agricultural Economists.
  30. Emiko Dupont & Simon N. Wood & Nicole H. Augustin, 2022. "Rejoinder to the discussions of “Spatial+: A novel approach to spatial confounding”," Biometrics, The International Biometric Society, vol. 78(4), pages 1309-1312, December.
  31. 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.
  32. Erin M. Schliep, 2018. "Comments on: Process modeling for slope and aspect with application to elevation data maps," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 778-782, December.
  33. 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.
  34. 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.
  35. 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.
  36. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
  37. Adam A. Szpiro & Lianne Sheppard & Sara D. Adar & Joel D. Kaufman, 2014. "Estimating acute air pollution health effects from cohort study data," Biometrics, The International Biometric Society, vol. 70(1), pages 164-174, March.
  38. Kyung-Min Nam & Yifu Ou & Euijune Kim & Siqi Zheng, 2022. "Air Pollution and Housing Values in Korea: A Hedonic Analysis with Long-range Transboundary Pollution as an Instrument," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 82(2), pages 383-407, June.
  39. 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.
  40. Daniel A. Griffith & Richard E. Plant, 2022. "Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects," Stats, MDPI, vol. 5(4), pages 1-20, December.
  41. Daniel A. Griffith, 2020. "A Family of Correlated Observations: From Independent to Strongly Interrelated Ones," Stats, MDPI, vol. 3(3), pages 1-19, June.
  42. Panczak, Radoslaw & Moser, André & Held, Leonhard & Jones, Philip A. & Rühli, Frank J. & Staub, Kaspar, 2017. "A tall order: Small area mapping and modelling of adult height among Swiss male conscripts," Economics & Human Biology, Elsevier, vol. 26(C), pages 61-69.
  43. 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.
  44. 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.
  45. Ephraim M. Hanks, 2017. "Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 497-507, April.
  46. 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.
  47. Eric Yanchenko & Howard D. Bondell & Brian J. Reich, 2024. "Spatial regression modeling via the R2D2 framework," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
  48. Daniel A. Griffith, 2019. "Negative Spatial Autocorrelation: One of the Most Neglected Concepts in Spatial Statistics," Stats, MDPI, vol. 2(3), pages 1-28, August.
  49. 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.
  50. Maitreyee Bose & James S. Hodges & Sudipto Banerjee, 2018. "Toward a diagnostic toolkit for linear models with Gaussian‐process distributed random effects," Biometrics, The International Biometric Society, vol. 74(3), pages 863-873, September.
  51. 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.
  52. 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.
  53. Neal Alexander & Audrey Lenhart & Karim Anaya-Izquierdo, 2020. "Spatial spillover analysis of a cluster-randomized trial against dengue vectors in Trujillo, Venezuela," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(9), pages 1-13, September.
  54. Daniel A. Griffith, 2024. "Comments on the Bernoulli Distribution and Hilbe’s Implicit Extra-Dispersion," Stats, MDPI, vol. 7(1), pages 1-15, March.
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