Bayesian prediction of clipped Gaussian random fields.
type: Item
instance of: scholarly article
title: Bayesian prediction of clipped Gaussian random fields.
zbMATH Open document ID: 1043.62079
DOI: 10.1016/S0167-9473(99)00103-6
published in: Computational Statistics and Data Analysis
publication date: 2000-10-26T00:00:00Z
review text: This work provides a framework to perform prediction in some types of binary random fields. It is assumed the binary random field is obtained by clipping a Gaussian random field at a fixed level. The model, following a Bayesian approach, is used to map a binary outcome over a bounded region \(D\) of the plane: For each location \(s_{0}\in D\), we compute the optimal predictor of \(Z(s_{0}), 0\) or 1, given the binary data from a realization of the random field, and provide measures of prediction uncertainty amenable for binary outcomes. The optimal predictor and the measure of prediction uncertainty are computed through data augmentation using Markov Chain Monte Carlo methods; a less computationally demanding plug-in approach is also described. A brief description of a geostatistical method called indicator kriging is given as well as some of its shortcomings. The prediction ability of the model is illustrated with two simulated binary maps, obtaining satisfactory results, and comparisons between the Bayesian, plug-in, and indicator kriging approaches are given.
Mathematics Subject Classification ID: 62M40
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 1521705
zbMATH Keywords: binary map
zbMATH Keywords: binary random field
zbMATH Keywords: data augmentation
zbMATH Keywords: indicator kriging
zbMATH Keywords: semivariogram
zbMATH Keywords: spatial prediction
author: Vinicius de Souza Lima Oliveira
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1521705