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Loss Function Approaches to Predict a Spatial Quantile and Its Exceedance Region

Summary: [This abstract is based on the authors' abstract.] It is important to predict a spatial exceedance and its exceedance region in the analysis of spatial data because unusual events can strongly affect the environment. Classes of loss functions based on image metrics are used here to predict the spatial exceedance region, and a joint loss is then proposed to predict a spatial quantile and its exceedance region. The optimal predictor is obtained by minimizing the posterior expected loss given the process parameters. The methodology is applied to a spatial data set of temperature changes over the Americas.

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  • Topics: Statistics
  • Keywords: Metrics, Data analysis, Bayesian methods, Geostatistics, Loss functions, Environmental management, Prediction
  • Author: Zhang, Jian; Craigmile, Peter F.; Cressie, Noel
  • Journal: Technometrics