A loss function approach to identifying environmental exceedances
type: Item
instance of: scholarly article
title: A loss function approach to identifying environmental exceedances
zbMATH Open document ID: 1115.62117
author: Peter F. Craigmile
author: Noel Cressie
author: Youlan Rao
author: Thomas Santner
published in: Extremes
publication date: 2007-05-29T00:00:00Z
review text: This article deals with the loss function approach to identifying environmental exceedances. The authors introduce the hierarchical spatial model and define the averaged cumulative distribution function and the integrated weighted quantile squared error loss function. There are presented several intuitively useful examples of integrated weighted quantile squared error loss functions for weight functions that can be used for estimating extrema and the authors calculate the optimal Bayes predictor relative to an arbitrary integrated weighted quantile squared error loss function. A hierarchical spatial model for pollutant concentrations based on the lognormal distribution is considered. Bayesian posterior inference derived from the model is discussed. An illustrating example shows how the methodology can be applied to the problem of estimating exceedances of surface-nitrogen concentrations in the Chesapeake Bay.
Mathematics Subject Classification ID: 62P12
Mathematics Subject Classification ID: 62M30
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 86A32
zbMATH DE Number: 5158673
zbMATH Keywords: Bayesian hierarchical models
zbMATH Keywords: geostatistics
zbMATH Keywords: integrated weighted quantile squared error loss
zbMATH Keywords: Bayes predictor
zbMATH Keywords: Markov chain Monte Carlo
reviewed by: Aleksandr D. Borisenko
describes a project that uses: ismev
MaRDI profile type: Publication
full work available at URL: s10687-006-7964-y
OpenAlex ID: W2022000828
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DOI: 10.1007/S10687-006-7964-Y
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description: scientific article; zbMATH DE number 5158673