Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-23T13:42:49.539Z Has data issue: false hasContentIssue false

Analogies and correspondences between variograms and covariance functions

Published online by Cambridge University Press:  01 July 2016

Tilmann Gneiting*
Affiliation:
University of Washington
Zoltán Sasvári*
Affiliation:
Technische Universität Dresden
Martin Schlather*
Affiliation:
Universität Bayreuth
*
Postal address: University of Washington, Department of Statistics, Box 354322, Seattle, Washington 98195-4322, USA. Email address: [email protected]
∗∗ Postal address: Technische Universität Dresden, Institut für Mathematische Stochastik, Mommsenstr. 13, 01602 Dresden, Germany.
∗∗∗ Postal address: Universität Bayreuth, Abteilung Bodenphysik, 95440 Bayreuth, Germany.

Abstract

Variograms and covariance functions are key tools in geostatistics. However, various properties, characterizations, and decomposition theorems have been established for covariance functions only. We present analogous results for variograms and explore the connections with covariance functions. Our findings include criteria for covariance functions on intervals, and we apply them to exponential models, fractional Brownian motion, and locally polynomial covariances. In particular, we characterize isotropic locally polynomial covariance functions of degree 3.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2001 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bisgaard, T. and Sasvári, Z. (2000). Characteristic Functions and Moment Sequences. Positive Definiteness in Probability, Vol. I. NOVA Science Publishers, Commack, NY.Google Scholar
Chilès, J.-P. and Delfiner, P. (1999). Geostatistics. Modeling Spatial Uncertainty. John Wiley, New York.Google Scholar
Cressie, N. A. C. (1993). Statistics for Spatial Data, Revised edn. John Wiley, New York.Google Scholar
Crum, M. M. (1956). On positive-definite functions. J. London Math. Soc. 6, 548560.CrossRefGoogle Scholar
Currin, C., Mitchell, T., Morris, M. and Ylvisaker, D. (1991). Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments. J. Amer. Statist. Assoc. 86, 953963.CrossRefGoogle Scholar
Davies, S. and Hall, P. (1999). Fractal analysis of surface roughness by using spatial data (with discussion). J. R. Statist. Soc. B 61, 337.Google Scholar
Gneiting, T. (1998). Simple tests for the validity of correlation function models on the circle. Statist. Prob. Lett. 39, 119122.Google Scholar
Gneiting, T. (1999a). Isotropic correlation functions on d-dimensional balls. Adv. Appl. Prob. 31, 625631.CrossRefGoogle Scholar
Gneiting, T. (1999b). On the derivatives of radial positive definite functions. J. Math. Anal. Appl. 236, 8693.CrossRefGoogle Scholar
Gneiting, T. (2000). Addendum to ‘Isotropic correlation functions on d-dimensional balls’. Adv. Appl. Prob. 32, 960961.Google Scholar
Gneiting, T. (2001). Criteria of Pólya type for radial positive definite functions. Proc. Amer. Math. Soc. 129, 23092318.Google Scholar
Gneiting, T. and Sasvári, Z. (1999). The characterization problem for isotropic covariance functions. Math. Geol. 31, 105111.Google Scholar
Keich, U. (2000). A possible definition of a stationary tangent. Stoch. Proc. Appl. 88, 136.Google Scholar
Krein, M. G. and Langer, H. (1985). On some continuation problems which are closely related to the theory of operators in spaces it Pi k. IV: Continuous analogues of orthogonal polynomials on the unit circle with respect to an indefinite weight and related continuation problems for some classes of functions. J. Operator Theory 13, 299417.Google Scholar
Mallat, S., Papanicolaou, G. and Zhang, Z. (1998). Adaptive covariance estimation and locally stationary processes. Ann. Statist. 26, 147.CrossRefGoogle Scholar
Matheron, G. (1973). The intrinsic random functions and their applications. Adv. Appl. Prob. 5, 439468.Google Scholar
Matheron, G. (1974). Représentations stationnaires et représentations minimales pour les FAI-k. Tech. Rept (Note Géostatistique 125), Centre de Géostatistique, École des Mines de Paris.Google Scholar
Mitchell, T., Morris, M. and Ylvisaker, D. (1990). Existence of smoothed stationary processes on an interval. Stoch. Proc. Appl. 35, 109119.CrossRefGoogle Scholar
Onn, S.-C., Wang, K.-H. and Tseng, C.-Y. (1994). Numerical simulation for the climatic statistical structure with the new correlation functions of the extended polynomial form and the extended exponential form. Adv. Space Res. 14 (9), 7781.CrossRefGoogle Scholar
Perrin, O. and Meiring, W. (1999). Identifiability for non-stationary spatial structure. J. Appl. Prob. 36, 12441250.Google Scholar
Perrin, O. and Senoussi, R. (1999). Reducing non-stationary stochastic processes to stationarity by a time deformation. Statist. Prob. Lett. 43, 393397.CrossRefGoogle Scholar
Perrin, O. and Senoussi, R. (2000). Reducing non-stationary random fields to stationarity and isotropy using a space deformation. Statist. Prob. Lett. 48, 2332.Google Scholar
Romanov, A. V. (1982). On sufficient conditions for positive definiteness of radial functions in domains of Rn . Soviet Math. Dokl. 26, 608613.Google Scholar
Sampson, P. D. and Guttorp, P. (1992). Nonparametric estimation of nonstationary spatial covariance structure. J. Amer. Statist. Assoc. 87, 108119.CrossRefGoogle Scholar
Sasvári, Z., (1994). Positive Definite and Definitizable Functions. Akademie, Berlin.Google Scholar
Stein, M. L. (2000). Fast and exact simulation of fractional Brownian surfaces. Tech. Rept 498, Department of Statistics, University of Chicago.Google Scholar
Wiencek, K. and Stoyan, D. (1993). Spatial correlations in metal structures and their analysis, II: the covariance. Materials Charact. 31, 4753.Google Scholar
Worsley, K., Evans, A. C., Strother, S. C. and Tyler, J. L. (1991). A linear spatial correlation model, with applications to positron emission tomography. J. Amer. Statist. Assoc. 86, 5567.CrossRefGoogle Scholar