Comparison of inference methods for estimating semivariogram model parameters and their uncertainty: the case of small data sets

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dc.contributor.author Pardo Iguzquiza, Eulogio
dc.contributor.author Dowd, Peter A.
dc.date.accessioned 2020-10-26T11:04:22Z
dc.date.available 2020-10-26T11:04:22Z
dc.date.issued 2012-06-12
dc.identifier.citation Computers & Geosciences, vol.50, 2013, 154-164 es_ES
dc.identifier.issn 1873-7803
dc.identifier.uri http://hdl.handle.net/20.500.12468/525
dc.description.abstract The semivariogram model is the fundamental component in all geostatistical applications and its inference is an issue of significant practical interest. The semivariogram model is defined by a mathematical function, the parameters of which are usually estimated from the experimental data. There are important application areas in which small data sets are the norm; rainfall estimation from rain gauge data and transmissivity estimation from pumping test data are two examples from, respectively, surface and subsurface hydrology. Thus a benchmark problem in geostatistics is deciding on the most appropriate method for the inference of the semivariogram model. The various methods for semivariogram inference can be classified as indirect methods, in which there is an intermediate step of calculating the experimental semivariogram, and direct approaches that obtain the model parameter values directly as the values that minimize some objective function. To avoid subjectivity in fitting models to experimental semivariograms, ordinary least squares (OLS), weighted least squares (WLS) and generalized least squares (GLS) are often used. Uncertainty evaluation in indirect methods is done using computationally intensive resampling procedures such as the bootstrap method. Direct methods include parametric methods, such as maximum likelihood (ML) and maximum likelihood cross-validation (MLCV), and non-parametric methods, such as minimization of cross-validation statistics (CV). The bases for comparing the previous methods are the sampling distribution of the various parameters and the “goodness” of the uncertainty evaluation in a sense that we define. The final questions to be answered are (1) which is the best method for estimating each of the semivariogram parameters? (2) Which is the best method for assessing the uncertainty of each of the parameters? (3) Which method best selects the functional form of the semivariogram from among a set of options? and (4) which is the best method that jointly addresses all the previous questions? es_ES
dc.description.sponsorship Instituto Geológico y Minero de España, España es_ES
dc.description.sponsorship Faculty of Engineering, Computer & Mathematical Sciences, University of Adelaide, Australia es_ES
dc.language.iso en es_ES
dc.publisher Elsevier es_ES
dc.relation CGL2010-15498 es_ES
dc.relation CGL2010-17629 es_ES
dc.relation DP110104766 es_ES
dc.rights Acceso abierto es_ES
dc.subject maximum likelihood es_ES
dc.subject cross-validation es_ES
dc.subject least squares es_ES
dc.subject chi-squared field es_ES
dc.subject root mean square error es_ES
dc.title Comparison of inference methods for estimating semivariogram model parameters and their uncertainty: the case of small data sets es_ES
dc.type Postprint es_ES
dc.relation.publisherversion https://reader.elsevier.com/reader/sd/pii/S0098300412001975?token=91F7C1B21509B2A1A5D605EE80F93678625305D7BC45CBEE3062F587557215967177543C0F69820B6404170CEF88739F es_ES
dc.description.funder Ministerio de Economía y Competitividad, España es_ES
dc.description.funder Australian Research Council, Australia es_ES
dc.identifier.doi http://dx.doi.org/10.1016/j.cageo.2012.06.002 es_ES


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