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Mean squared prediction error in the spatial linear model with estimated covariance parameters

Author

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  • Dale Zimmerman
  • Noel Cressie

Abstract

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Suggested Citation

  • Dale Zimmerman & Noel Cressie, 1992. "Mean squared prediction error in the spatial linear model with estimated covariance parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 27-43, March.
  • Handle: RePEc:spr:aistmt:v:44:y:1992:i:1:p:27-43
    DOI: 10.1007/BF00048668
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    Citations

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    Cited by:

    1. Iranpanah, N. & Mohammadzadeh, M. & Taylor, C.C., 2011. "A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 578-587, January.
    2. Huang, Whitney K. & Chung, Yu-Min & Wang, Yu-Bo & Mandel, Jeff E. & Wu, Hau-Tieng, 2022. "Airflow recovery from thoracic and abdominal movements using synchrosqueezing transform and locally stationary Gaussian process regression," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    3. William F. Christensen, 2011. "Filtered Kriging for Spatial Data with Heterogeneous Measurement Error Variances," Biometrics, The International Biometric Society, vol. 67(3), pages 947-957, September.
    4. Kęstutis Dučinskas & Lina Dreižienė, 2018. "Risks of Classification of the Gaussian Markov Random Field Observations," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 422-436, October.
    5. Werner Müller & Milan Stehlík, 2009. "Issues in the optimal design of computer simulation experiments," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(2), pages 163-177, March.
    6. Monica Pratesi & Nicola Salvati, 2008. "Small area estimation: the EBLUP estimator based on spatially correlated random area effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 113-141, February.
    7. C. Helbert & D. Dupuy & L. Carraro, 2009. "Assessment of uncertainty in computer experiments from Universal to Bayesian Kriging," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(2), pages 99-113, March.

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