caa a22 4500 46792256X CHVBK 20180406152921.0 cr unu---uuuuu 170328e20060101xx s 000 0 eng 10.1007/s00477-005-0004-7 doi (NATIONALLICENCE)springer-10.1007/s00477-005-0004-7 Emery Xavier Department of Mining Engineering, University of Chile, Avenida Tupper 2069, Santiago, Chile aut Multigaussian kriging for point-support estimation: incorporating constraints on the sum of the kriging weights [Elektronische Daten] [Xavier Emery] In the geostatistical analysis of regionalized data, the practitioner may not be interested in mapping the unsampled values of the variable that has been monitored, but in assessing the risk that these values exceed or fall short of a regulatory threshold. This kind of concern is part of the more general problem of estimating a transfer function of the variable under study. In this paper, we focus on the multigaussian model, for which the regionalized variable can be represented (up to a nonlinear transformation) by a Gaussian random field. Two cases are analyzed, depending on whether the mean of this Gaussian field is considered known or not, which lead to the simple and ordinary multigaussian kriging estimators respectively. Although both of these estimators are theoretically unbiased, the latter may be preferred to the former for practical applications since it is robust to a misspecification of the mean value over the domain of interest and also to local fluctuations around this mean value. An advantage of multigaussian kriging over other nonlinear geostatistical methods such as indicator and disjunctive kriging is that it makes use of the multivariate distribution of the available data and does not produce order relation violations. The use of expansions into Hermite polynomials provides three additional results: first, an expression of the multigaussian kriging estimators in terms of series that can be calculated without numerical integration; second, an expression of the associated estimation variances; third, the derivation of a disjunctive-type estimator that minimizes the variance of the error when the mean is unknown. Springer-Verlag, 2005 Gaussian random fields nationallicence Multivariate normality nationallicence Conditional expectation nationallicence Ordinary kriging nationallicence Lognormal kriging nationallicence Hermite polynomials nationallicence Stochastic Environmental Research and Risk Assessment Springer-Verlag 20/1-2(2006-01-01), 53-65 1436-3240 20:1-2<53 2006 20 477 https://doi.org/10.1007/s00477-005-0004-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00477-005-0004-7 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Emery Xavier Department of Mining Engineering, University of Chile, Avenida Tupper 2069, Santiago, Chile aut NATIONALLICENCE 773 0- Stochastic Environmental Research and Risk Assessment Springer-Verlag 20/1-2(2006-01-01), 53-65 1436-3240 20:1-2<53 2006 20 477 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer