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Thió i Fernández de Henestrosa, Santiago
MartÃn Fernández, Josep Antoni |
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Universitat de Girona. Departament d’Informà tica i Matemà tica Aplicada | |
Tolosana Delgado, Raimon
Pawlowsky Glahn, Vera |
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Kriging is an interpolation technique whose optimality criteria are based on normality assumptions either for observed or for transformed data. This is the case of normal, lognormal and multigaussian kriging.When kriging is applied to transformed scores, optimality of obtained estimators becomes a cumbersome concept: back-transformed optimal interpolations in transformed scores are not optimal in the original sample space, and vice-versa. This lack of compatible criteria of optimality induces a variety of problems in both point and block estimates. For instance, lognormal kriging, widely used to interpolate positivevariables, has no straightforward way to build consistent and optimal confidence intervals for estimates.These problems are ultimately linked to the assumed space structure of the data support: for instance, positive values, when modelled with lognormal distributions, are assumed to be embedded in the whole real space, with the usual real space structure and Lebesgue measure Geologische Vereinigung; Universitat de Barcelona, Equip de Recerca Arqueomètrica; Institut d’EstadÃstica de Catalunya; International Association for Mathematical Geology; Patronat de l’Escola Politècnica Superior de la Universitat de Girona; Fundació privada: Girona, Universitat i Futur. |
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http://hdl.handle.net/2072/14695 | |
eng | |
Universitat de Girona. Departament d’Informà tica i Matemà tica Aplicada | |
Tots els drets reservats | |
Anà lisi multivariable
Geologia -- Mètodes estadÃstics |
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Kriging coordinates: what does that mean? | |
info:eu-repo/semantics/conferenceObject | |
Recercat |