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Activity Number:
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40
- Modern and Innovative Spatial Methods in Ecology and the Environment
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Type:
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Invited
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Date/Time:
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Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
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Sponsor:
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JABES-Journal of Agricultural, Biological, and Environmental Statistics
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Abstract #320565
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Title:
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Spatial Generalized Linear Models with Non-Gaussian Translation Processes
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Author(s):
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Robert Richardson*
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Companies:
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Brigham Young University
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Keywords:
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Spatial Copula;
Non-Gaussian Processes;
Bayeisan Spatio-Temporal Model;
Power Truncated Normal;
Nearest Neighbor Gaussian Process
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Abstract:
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This paper introduces an adjustment to spatial copula processes called a non-Gaussian translation process that will allow for the specification of any marginal distribution with a closed-form density function in a single unified framework. While translation processes do not preserve the exact marginal structure, they allow for fixed effects to be included in a non-Gaussian spatial model without needing to define a link function, as well as providing a number of other computational and modeling benefits. Non-Gaussian translation processes are compared theoretically and via simulation with traditional link function approaches and spatial copula processes and are shown to perform similarly in cases where all three models can effectively be used. A daily precipitation data set is analyzed with elevation as a predictor variable that includes a majority of observations being 0. Out-of-sample predictions are evaluated, and it is determined that the model is effective when compared to a two-stage predictionmodel and a Bayesian power truncated normal model.
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Authors who are presenting talks have a * after their name.
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