Kriging and masurement errors

• Autorzy: István Fazekas , Alexander G. Kukush
• Języki publikacji: EN

Abstrakty

EN

A linear geostatistical model is considered. Properties of a universal kriging are studied when the locations of observations aremeasured with errors. Alternative prediction procedures are introduced and their least squares errors are analyzed.

Słowa kluczowe

EN

universal kriging; least squares; errors-in-variables

Kategorie tematyczne

• 62M30: Spatial processes

• Wydawca: Faculty of Mathematics, Computer Science and Econometrics, University of Zielona Góra
• Czasopismo: Discussiones Mathematicae Probability and Statistics
• Rocznik: 2005
• Tom: 25
• Numer: 2
• Strony: 139-159

Daty

wydano

2005

otrzymano

2004-01-10

poprawiono

2005-08-02

Twórcy

autor

István Fazekas

• Faculty of Informatics, University of Debrecen, P.O. Box 12, H-4010 Debrecen, Hungary

autor

Alexander G. Kukush

• Department of Mathematics and Mechanics, Kiev University, Vladimirskaya st. 64, 252601 Kiev, Ukraine

Bibliografia

• [1] O. Berke, On spatiotemporal prediction for on-line monitoring data, Comm. Statist. Theory Methods 27 (9) (1998), 2343-2369.
• [2] N.A.C. Cressie, Statistics for Spatial Data, Wiley, New York 1991.
• [3] I. Fazekas, S. Baran, A.G. Kukush, and J. Lauridsen, Asymptotic properties in space and time of an estimator in nonlinear functional errors-in-variables models, Random Oper. Stoch. Equ. 7 (4) (1999), 389-412.
• [4] I. Fazekas and A.G. Kukush, Errors-in-variables and kriging, Proc. 4th International Conference on Applied Informatics, Eger 1999, 261-273.
• [5] J. Gabrosek and N. Cressie, The effect on attribute prediction of locationuncertainity in spatial data, Geographical Analysis 34 (3) (2002), 261-285.
• [6] D.G. Krige, A statistical approach to some basic mine valuations problems on the Witwatersrand, Journal of the Chemical, Metallurgical and Mining Society of South Africa 52 (1951), 119-139.
• [7] S.J. Yakowitz and F. Szidarovszky, A comparison of kriging with nonparametric regression methods, J. Multivariate Anal. 16 (1985), 21-53.

Identyfikatory

DOI

10.7151/dmps.1066