Spatial Bayesian Nonparametric Approach for Extreme Temperatures. Abstract - "Extreme temperature trends across space and time, especially those of minimum temperature, are not well understood and yet are important for studying climate change. We extend common statistical models for extreme data to the spatial setting using a nonparametric approach by introducing a Dirichelet-type mixture model, with marginals that have generalized extreme value (GEV) distributions. The GEV parameters are allowed to vary spatially and temporally, however this may not explain all spatial correlation. Our proposed methodology is able to capture the unexplained spatial dependence after accounting for the GEV spatial parameters while also allowing for nonstationarity. This modeling approach provides flexibility to characterize complex spatial dependence between the extreme values at the sites. Our approach is computationally efficient, since it avoids matrix inversions used in the common copula frameworks for spatial extremes. We apply our nonparametric spatial methodology to minimum temperature data in the Midwest region of the United States."
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