A service provided by the WU Library and the WU IT-Services

Optimal Design for Variogram Estimation

Müller, Werner and Zimmerman, Dale L. (1997) Optimal Design for Variogram Estimation. Forschungsberichte / Institut für Statistik, 51. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.

[img]
Preview
PDF
Download (1057Kb) | Preview

Abstract

The variogram plays a central role in the analysis of geostatistical data. A valid variogram model is selected and the parameters of that model are estimated before kriging (spatial prediction) is performed. These inference procedures are generally based upon examination of the empirical variogram, which consists of average squared differences of data taken at sites lagged the same distance apart in the same direction. The ability of the analyst to estimate variogram parameters efficiently is affected significantly by the sampling design, i.e., the spatial configuration of sites where measurements are taken. In this paper, we propose design criteria that, in contrast to some previously proposed criteria oriented towards kriging with a known variogram, emphasize the accurate estimation of the variogram. These criteria are modifications of design criteria that are popular in the context of (nonlinear) regression models. The two main distinguishing features of the present context are that the addition of a single site to the design produces as many new lags as there are existing sites and hence also produces that many new squared differences from which the variograrn is estimated. Secondly, those squared differences are generally correlated, which inhibits the use of many standard design methods that rest upon the assumption of uncorrelated errors. Several approaches to design construction which account for these features are described and illustrated with two examples. We compare their efficiency to simple random sampling and regular and space-filling designs and find considerable improvements. (author's abstract)

Item Type: Paper
Keywords: D-Optimality / Geostatistics / Kriging / Spatial dependence
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 11 Jul 2006 13:24
Last Modified: 26 Jul 2015 02:20
URI: http://epub.wu.ac.at/id/eprint/756

Actions

View Item