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Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets

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  • Andrew O. Finley
  • Sudipto Banerjee
  • Patrik Waldmann
  • Tore Ericsson

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  • 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.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:2:p:441-451
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01115.x
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    References listed on IDEAS

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    1. Montserrat Fuentes, 2002. "Spectral methods for nonstationary spatial processes," Biometrika, Biometrika Trust, vol. 89(1), pages 197-210, March.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    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. Michael L. Stein & Zhiyi Chi & Leah J. Welty, 2004. "Approximating likelihoods for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 275-296, May.
    5. 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.
    6. 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.
    7. 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.
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    Cited by:

    1. Çiğdem Ak & Önder Ergönül & İrfan Şencan & Mehmet Ali Torunoğlu & Mehmet Gönen, 2018. "Spatiotemporal prediction of infectious diseases using structured Gaussian processes with application to Crimean–Congo hemorrhagic fever," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 12(8), pages 1-20, August.
    2. Massimo Ventrucci & Daniela Cocchi & Gemma Burgazzi & Alex Laini, 2020. "PC priors for residual correlation parameters in one-factor mixed models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 745-765, December.
    3. Guhaniyogi, Rajarshi & Banerjee, Sudipto, 2019. "Multivariate spatial meta kriging," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 3-8.
    4. Steen MAGNUSSEN, 2018. "An estimation strategy to protect against over-estimating precision in a LiDAR-based prediction of a stand mean," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 64(12), pages 497-505.
    5. Maitreyee Bose & James S. Hodges & Sudipto Banerjee, 2018. "Toward a diagnostic toolkit for linear models with Gaussian‐process distributed random effects," Biometrics, The International Biometric Society, vol. 74(3), pages 863-873, September.

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