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Combining outputs from the North American Regional Climate Change Assessment Program by using a Bayesian hierarchical model

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  • Emily L. Kang
  • Noel Cressie
  • Stephan R. Sain

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  • Emily L. Kang & Noel Cressie & Stephan R. Sain, 2012. "Combining outputs from the North American Regional Climate Change Assessment Program by using a Bayesian hierarchical model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 291-313, March.
    • Handle: RePEc:bla:jorssc:v:61:y:2012:i:2:p:291-313
    DOI: j.1467-9876.2011.01010.x
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    File URL: http://hdl.handle.net/10.1111/j.1467-9876.2011.01010.x
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    Cited by:

    1. Jun, Mikyoung, 2014. "Matérn-based nonstationary cross-covariance models for global processes," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 134-146.
    2. Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
    3. Alexandra Jonko & Nathan M. Urban & Balu Nadiga, 2018. "Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data," Climatic Change, Springer, vol. 149(2), pages 247-260, July.
    4. Ashton Wiens & Douglas Nychka & William Kleiber, 2020. "Modeling spatial data using local likelihood estimation and a Matérn to spatial autoregressive translation," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.

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