Loss function approaches to predict a spatial quantile and its exceedance region

Zhang, J., Craigmile, P.F. and Cressie, N. (2008) Loss function approaches to predict a spatial quantile and its exceedance region. Technometrics, 50(2), pp. 216-227. (doi: 10.1198/004017008000000226)

Abstract

In the analysis of spatial data, it is common to predict a spatial exceedance and its associated exceedance region. This is scientifically important, because unusual events tend to strongly affect the environment. We use classes of loss functions based on image metrics (e.g., Baddeley's loss function) to predict the spatial-exceedance region. We then propose a joint loss to predict a spatial quantile and its exceedance region. The optimal predictor is obtained by minimizing the posterior expected loss given the process parameters, which we achieve by simulated annealing. Various predictors are compared through simulation. This methodology is applied to a spatial data set of temperature change over the Americas.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Craigmile, Dr Peter
• Authors: Zhang, J., Craigmile, P.F., and Cressie, N.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Technometrics
• ISSN: 0040-1706
• ISSN (Online): 1537-2723
• Published Online: 01 January 2012