IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v74y2012i1p111-132.html
   My bibliography  Save this article

A full scale approximation of covariance functions for large spatial data sets

Author

Listed:
  • Huiyan Sang
  • Jianhua Z. Huang

Abstract

No abstract is available for this item.

Suggested Citation

  • Huiyan Sang & Jianhua Z. Huang, 2012. "A full scale approximation of covariance functions for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 111-132, January.
    • Handle: RePEc:bla:jorssb:v:74:y:2012:i:1:p:111-132
    DOI: j.1467-9868.2011.01007.x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1467-9868.2011.01007.x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/j.1467-9868.2011.01007.x?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.

    Citations

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


    Cited by:

    1. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    2. Paciorek, Christopher J. & Lipshitz, Benjamin & Zhuo, Wei & Prabhat, . & Kaufman, Cari G. G. & Thomas, Rollin C., 2015. "Parallelizing Gaussian Process Calculations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i10).
    3. 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.
    4. Jun, Mikyoung, 2014. "Matérn-based nonstationary cross-covariance models for global processes," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 134-146.
    5. Hu Juan & Zhang Hao, 2015. "Numerical Methods of Karhunen–Loève Expansion for Spatial Data," Stochastics and Quality Control, De Gruyter, vol. 30(1), pages 49-58, June.
    6. Kelly R. Moran & Matthew W. Wheeler, 2022. "Fast increased fidelity samplers for approximate Bayesian Gaussian process regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1198-1228, September.
    7. Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2013. "Efficient Approximation of the Spatial Covariance Function for Large Datasets - Analysis of Atmospheric CO2 Concentrations," Discussion Paper Series RECAP15 009, RECAP15, European University Viadrina, Frankfurt (Oder).
    8. Peter A. Gao & Hannah M. Director & Cecilia M. Bitz & Adrian E. Raftery, 2022. "Probabilistic Forecasts of Arctic Sea Ice Thickness," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 280-302, June.
    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. 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.
    11. 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.
    12. Miguel A Campo-Bescós & Rafael Muñoz-Carpena & David A Kaplan & Jane Southworth & Likai Zhu & Peter R Waylen, 2013. "Beyond Precipitation: Physiographic Gradients Dictate the Relative Importance of Environmental Drivers on Savanna Vegetation," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-14, August.
    13. Arthur P. Guillaumin & Adam M. Sykulski & Sofia C. Olhede & Frederik J. Simons, 2022. "The Debiased Spatial Whittle likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1526-1557, September.
    14. Bledar A. Konomi & Emily L. Kang & Ayat Almomani & Jonathan Hobbs, 2023. "Bayesian Latent Variable Co-kriging Model in Remote Sensing for Quality Flagged Observations," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(3), pages 423-441, September.
    15. Shinichiro Shirota & Andrew O. Finley & Bruce D. Cook & Sudipto Banerjee, 2023. "Conjugate sparse plus low rank models for efficient Bayesian interpolation of large spatial data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    16. Sandra De Iaco & Donato Posa & Claudia Cappello & Sabrina Maggio, 2021. "On Some Characteristics of Gaussian Covariance Functions," International Statistical Review, International Statistical Institute, vol. 89(1), pages 36-53, April.
    17. Waley W. J. Liang & Herbert K. H. Lee, 2019. "Bayesian nonstationary Gaussian process models via treed process convolutions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 797-818, September.
    18. Jingjing Yang & Dennis D. Cox & Jong Soo Lee & Peng Ren & Taeryon Choi, 2017. "Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian–Wishart processes," Biometrics, The International Biometric Society, vol. 73(4), pages 1082-1091, December.
    19. Harrison Quick & Sudipto Banerjee & Bradley P. Carlin, 2015. "Bayesian modeling and analysis for gradients in spatiotemporal processes," Biometrics, The International Biometric Society, vol. 71(3), pages 575-584, September.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssb:v:74:y:2012:i:1:p:111-132. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.