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Modeling nonstationarity in space and time

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  • Lyndsay Shand
  • Bo Li

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  • Lyndsay Shand & Bo Li, 2017. "Modeling nonstationarity in space and time," Biometrics, The International Biometric Society, vol. 73(3), pages 759-768, September.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:3:p:759-768
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    File URL: http://hdl.handle.net/10.1111/biom.12656
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    References listed on IDEAS

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    1. 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.
    2. Alexandra M. Schmidt & Anthony O'Hagan, 2003. "Bayesian inference for non‐stationary spatial covariance structure via spatial deformations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 743-758, August.
    3. 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.
    4. LI, Bo & Genton, Marc G. & Sherman, Michael, 2007. "A Nonparametric Assessment of Properties of SpaceTime Covariance Functions," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 736-744, June.
    5. Michael L. Stein, 2005. "Space-Time Covariance Functions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 310-321, March.
    6. Luke Bornn & Gavin Shaddick & James V. Zidek, 2012. "Modeling Nonstationary Processes Through Dimension Expansion," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 281-289, March.
    7. 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.
    8. Perrin, Olivier & Schlather, Martin, 2007. "Can any multivariate gaussian vector be interpreted as a sample from a stationary random process?," Statistics & Probability Letters, Elsevier, vol. 77(9), pages 881-884, May.
    9. Montserrat Fuentes, 2002. "Spectral methods for nonstationary spatial processes," Biometrika, Biometrika Trust, vol. 89(1), pages 197-210, March.
    10. Mark D. Risser & Catherine A. Calder, 2015. "Regression‐based covariance functions for nonstationary spatial modeling," Environmetrics, John Wiley & Sons, Ltd., vol. 26(4), pages 284-297, June.
    11. 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.
    12. Matthias Katzfuss, 2013. "Bayesian nonstationary spatial modeling for very large datasets," Environmetrics, John Wiley & Sons, Ltd., vol. 24(3), pages 189-200, May.
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    Cited by:

    1. Kai Yang & Peihua Qiu, 2022. "A three-step local smoothing approach for estimating the mean and covariance functions of spatio-temporal Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 49-68, February.

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