Modelling Spatio-Temporal Variation in Sparse Rainfall Data Using a Hierarchical Bayesian Regression Model-Reference-Cited by-同舟云学术

Modelling Spatio-Temporal Variation in Sparse Rainfall Data Using a Hierarchical Bayesian Regression Model

Published:2019-03-25 Issue:2 Volume:24 Page:369-393
ISSN:1085-7117
Container-title:Journal of Agricultural, Biological and Environmental Statistics
language:en
Short-container-title:JABES

Author:

Mukhopadhyay Sabyasachi^ORCID,Ogutu Joseph O.,Bartzke Gundula,Dublin Holly T.,Piepho Hans-Peter

Funder

European Unions Horizon 2020

Deutsche Forschungsgemeinschaft

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,Agricultural and Biological Sciences (miscellaneous),General Environmental Science,Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s13253-019-00357-3.pdf

Reference47 articles.

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3. Banerjee, S., Gelfand, A. E., Finley, A. O., and Sang, H. (2008), “Gaussian predictive process models for large spatial data sets,” Journal of Royal Statistical Society, Series B, 70, 825–848.

4. Bartzke, G. S., Ogutu, J. O., Mukhopadhyay, S., Mtui, D., Dublin, H. T., and Piepho, H. P. (2018), “Rainfall trends and variation in the Maasai Mara ecosystem and their implications for animal population and biodiversity dynamics,” PLoS ONE, 13(9), e0202814.

5. Boutton, T. W., Tieszen, L. L., and Imbamba, S. K. (1988a),“Biomass dynamics of grassland vegetation in Kenya,” AfricanJournal of Ecology, 26, 89–101.

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