IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v14y2012i1p29-47.html
   My bibliography  Save this article

Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes

Author

Listed:
  • Andrew Finley
  • Sudipto Banerjee
  • Alan Gelfand

Abstract

No abstract is available for this item.

Suggested Citation

  • Andrew Finley & Sudipto Banerjee & Alan Gelfand, 2012. "Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes," Journal of Geographical Systems, Springer, vol. 14(1), pages 29-47, January.
    • Handle: RePEc:kap:jgeosy:v:14:y:2012:i:1:p:29-47
    DOI: 10.1007/s10109-011-0154-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10109-011-0154-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10109-011-0154-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kaufman, Cari G. & Schervish, Mark J. & Nychka, Douglas W., 2008. "Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1545-1555.
    2. R. Pace & James LeSage, 2009. "A sampling approach to estimate the log determinant used in spatial likelihood problems," Journal of Geographical Systems, Springer, vol. 11(3), pages 209-225, September.
    3. Fuentes, Montserrat, 2007. "Approximate Likelihood for Large Irregularly Spaced Spatial Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 321-331, March.
    4. Crainiceanu, Ciprian M. & Diggle, Peter J. & Rowlingson, Barry, 2008. "Bivariate Binomial Spatial Modeling of Loa loa Prevalence in Tropical Africa," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 21-37, March.
    5. Finley, Andrew O. & Sang, Huiyan & Banerjee, Sudipto & Gelfand, Alan E., 2009. "Improving the performance of predictive process modeling for large datasets," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2873-2884, June.
    6. Jonathan R. Stroud & Peter Müller & Bruno Sansó, 2001. "Dynamic models for spatiotemporal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 673-689.
    7. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    8. Smith, Richard L. & Tebaldi, Claudia & Nychka, Doug & Mearns, Linda O., 2009. "Bayesian Modeling of Uncertainty in Ensembles of Climate Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 97-116.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ting Fung Ma & Fangfang Wang & Jun Zhu & Anthony R. Ives & Katarzyna E. Lewińska, 2023. "Scalable Semiparametric Spatio-temporal Regression for Large Data Analysis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 279-298, June.
    2. Alejandro Almeida & Antonio Golpe & Raquel Justo, 2021. "From hot to cold: A spatial analysis of self‐employment in the United States," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1005-1023, August.
    3. John O'Sullivan & Conor Sweeney & Andrew C. Parnell, 2020. "Bayesian spatial extreme value analysis of maximum temperatures in County Dublin, Ireland," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    4. Finley, Andrew O. & Banerjee, Sudipto & Gelfand, Alan E., 2015. "spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i13).
    5. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    6. Joshua S. North & Erin M. Schliep & Christopher K. Wikle, 2021. "On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    7. Ying Liu & Dootika Vats & James M. Flegal, 2022. "Batch Size Selection for Variance Estimators in MCMC," Methodology and Computing in Applied Probability, Springer, vol. 24(1), pages 65-93, March.
    8. Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eidsvik, Jo & Finley, Andrew O. & Banerjee, Sudipto & Rue, Håvard, 2012. "Approximate Bayesian inference for large spatial datasets using predictive process models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1362-1380.
    2. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    3. Giovanna Jona Lasinio & Gianluca Mastrantonio & Alessio Pollice, 2013. "Discussing the “big n problem”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 97-112, March.
    4. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    5. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    6. Jialuo Liu & Tingjin Chu & Jun Zhu & Haonan Wang, 2022. "Large spatial data modeling and analysis: A Krylov subspace approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1115-1143, September.
    7. Zilber, Daniel & Katzfuss, Matthias, 2021. "Vecchia–Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    8. K. Shuvo Bakar, 2020. "Interpolation of daily rainfall data using censored Bayesian spatially varying model," Computational Statistics, Springer, vol. 35(1), pages 135-152, March.
    9. Litvinenko, Alexander & Sun, Ying & Genton, Marc G. & Keyes, David E., 2019. "Likelihood approximation with hierarchical matrices for large spatial datasets," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 115-132.
    10. Padoan, Simone A. & Bevilacqua, Moreno, 2015. "Analysis of Random Fields Using CompRandFld," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i09).
    11. Matthias Katzfuss, 2017. "A Multi-Resolution Approximation for Massive Spatial Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 201-214, January.
    12. Ryan J. Parker & Brian J. Reich & Jo Eidsvik, 2016. "A Fused Lasso Approach to Nonstationary Spatial Covariance Estimation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 569-587, September.
    13. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    14. Ranadeep Daw & Christopher K. Wikle, 2023. "REDS: Random ensemble deep spatial prediction," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    15. Marchetti, Yuliya & Nguyen, Hai & Braverman, Amy & Cressie, Noel, 2018. "Spatial data compression via adaptive dispersion clustering," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 138-153.
    16. Jonathan Bradley & Noel Cressie & Tao Shi, 2015. "Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 1-28, March.
    17. Mahdi Hosseinpouri & Majid Jafari Khaledi, 2019. "An area-specific stick breaking process for spatial data," Statistical Papers, Springer, vol. 60(1), pages 199-221, February.
    18. Huang Huang & Sameh Abdulah & Ying Sun & Hatem Ltaief & David E. Keyes & Marc G. Genton, 2021. "Competition on Spatial Statistics for Large Datasets," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 580-595, December.
    19. Furrer, Reinhard & Bachoc, François & Du, Juan, 2016. "Asymptotic properties of multivariate tapering for estimation and prediction," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 177-191.
    20. Morales-Oñate, Víctor & Crudu, Federico & Bevilacqua, Moreno, 2021. "Blockwise Euclidean likelihood for spatio-temporal covariance models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 176-201.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jgeosy:v:14:y:2012:i:1:p:29-47. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.