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Computational techniques for spatial logistic regression with large data sets

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  • Paciorek, Christopher J.

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  • Paciorek, Christopher J., 2007. "Computational techniques for spatial logistic regression with large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3631-3653, May.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:8:p:3631-3653
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    1. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    2. Zhang, Hao, 2004. "Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 250-261, January.
    3. C. G. Wager & B. A. Coull & N. Lange, 2004. "Modelling spatial intensity for replicated inhomogeneous point patterns in brain imaging," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 429-446, May.
    4. S. N. Wood, 2000. "Modelling and smoothing parameter estimation with multiple quadratic penalties," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 413-428.
    5. Hååvard Rue & Hååkon Tjelmeland, 2002. "Fitting Gaussian Markov Random Fields to Gaussian Fields," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 31-49, March.
    6. Andrew B. Lawson, 1993. "On the Analysis of Mortality Events Associated with a Prespecified Fixed Point," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(3), pages 363-377, May.
    7. Sally A. Wood, 2002. "Bayesian mixture of splines for spatially adaptive nonparametric regression," Biometrika, Biometrika Trust, vol. 89(3), pages 513-528, August.
    8. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    9. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    10. Ngo, Long & Wand, Matthew P., 2004. "Smoothing with Mixed Model Software," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i01).
    11. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    12. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
    13. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    14. P. J. Lenk, 1999. "Bayesian inference for semiparametric regression using a Fourier representation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 863-879.
    15. Yi Li & Louise Ryan, 2002. "Modeling Spatial Survival Data Using Semiparametric Frailty Models," Biometrics, The International Biometric Society, vol. 58(2), pages 287-297, June.
    16. Peter J. Diggle, 1990. "A Point Process Modelling Approach to Raised Incidence of a Rare Phenomenon in the Vicinity of a Prespecified Point," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(3), pages 349-362, May.
    17. Leonhard Knorr‐Held & Håvard Rue, 2002. "On Block Updating in Markov Random Field Models for Disease Mapping," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 597-614, December.
    18. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    19. Ole F. Christensen & Rasmus Waagepetersen, 2002. "Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 58(2), pages 280-286, June.
    20. E. E. Kammann & M. P. Wand, 2003. "Geoadditive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 1-18, January.
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    Cited by:

    1. Vahid Tadayon & Mohammad Mehdi Saber, 2023. "A Spatial Logistic Regression Model Based on a Valid Skew-Gaussian Latent Field," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 59-73, March.
    2. Berrett, Candace & Calder, Catherine A., 2012. "Data augmentation strategies for the Bayesian spatial probit regression model," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 478-490.
    3. Dorota Młynarczyk & Carmen Armero & Virgilio Gómez-Rubio & Pedro Puig, 2021. "Bayesian Analysis of Population Health Data," Mathematics, MDPI, vol. 9(5), pages 1-15, March.
    4. Jo Eidsvik & Sara Martino & Håvard Rue, 2009. "Approximate Bayesian Inference in Spatial Generalized Linear Mixed Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 1-22, March.
    5. Andrew O. Finley & Sudipto Banerjee & Patrik Waldmann & Tore Ericsson, 2009. "Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets," Biometrics, The International Biometric Society, vol. 65(2), pages 441-451, June.
    6. Gschlößl, Susanne & Czado, Claudia, 2008. "Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4184-4202, May.
    7. Qian Ren & Sudipto Banerjee, 2013. "Hierarchical Factor Models for Large Spatially Misaligned Data: A Low-Rank Predictive Process Approach," Biometrics, The International Biometric Society, vol. 69(1), pages 19-30, March.
    8. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    9. 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.
    10. Ren, Qian & Banerjee, Sudipto & Finley, Andrew O. & Hodges, James S., 2011. "Variational Bayesian methods for spatial data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3197-3217, December.
    11. Weimann, Amy & Dai, Dajun & Oni, Tolu, 2016. "A cross-sectional and spatial analysis of the prevalence of multimorbidity and its association with socioeconomic disadvantage in South Africa: A comparison between 2008 and 2012," Social Science & Medicine, Elsevier, vol. 163(C), pages 144-156.
    12. Kwideok Han & Meilan An & Inbae Ji, 2021. "Analyzing Spatial Dependency of the 2016–2017 Korean HPAI Outbreak to Determine the Effective Culling Radius," IJERPH, MDPI, vol. 18(18), pages 1-12, September.
    13. Liang, Shengde & Banerjee, Sudipto & Bushhouse, Sally & Finley, Andrew O. & Carlin, Bradley P., 2008. "Hierarchical multiresolution approaches for dense point-level breast cancer treatment data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2650-2668, January.
    14. Zoe Gibbs & Chris Groendyke & Brian Hartman & Robert Richardson, 2020. "Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models," Risks, MDPI, vol. 8(4), pages 1-15, November.
    15. repec:jss:jstsof:19:i02 is not listed on IDEAS

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