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GY SAMPLING THEORY AND GEOSTATISTICS: ALTERNATE MODELS OF VARIABILITY IN CONTINUOUS MEDIA
Citation:
Englund, E J. GY SAMPLING THEORY AND GEOSTATISTICS: ALTERNATE MODELS OF VARIABILITY IN CONTINUOUS MEDIA. Presented at American Statistical Association, Atlanta, GA, August 5-9, 2001.
Impact/Purpose:
The overall objective of the chemometrics and environmetrics program and this task is to examine and evaluate the statistical procedures and methods used in the measurement or experimentation process and to improve those procedures and methods (if deemed inadequate) by investigating, developing, and evaluating statistical methods, algorithms, and software to reduce data uncertainty. The measurement or experimentation process encompasses: decision objectives and design, sampling design, sampling, experimental design, quality control, data collection, signal processing and data manipulation, data analysis, validation, and decision analysis. Other general objectives of the program are to: evaluate certain existing, developed, or potential performance measurements for information content, relevancy, and cost-effectiveness. The objectives of the sampling research area are to provide the Agency with improved state-of-the-science guidance, strategies, and techniques to more accurately and effectively collect solid particulate field and laboratory subsamples that best represent the extent and degree of contamination at a given site.
Description:
In the sampling theory developed by Pierre Gy, sample variability is modeled as the sum of a set of seven discrete error components. The variogram used in geostatisties provides an alternate model in which several of Gy's error components are combined in a continuous model of spatial variability. Both models describe essentially the same total variability in a continuous medium. Gy's model emphasizes micro scale, or within-sample variability while the variogram emphasizes between-sample variability.
An interesting and useful feature of the variogram model is that it permits computation of the variance of samples within any arbitrary larger volume. When that larger volume reflects a proposed increase in sample mass, the sample-within-volume variance is the variance reduction that can be expected from the mass increase. Exact computation of sample-within-volume variances is difficult but they can be easily estimated visually from the variogram model using simple rules-of-thumb.