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RECORD NUMBER: 236 OF 1390

OLS Field Name OLS Field Data
Main Title Empirical General Population Assessment of the Variance and Variance Estimators of the Horvitz-Thompson Estimator under Variable Probability Sampling.
Author Stehman, S. V. ; Overton, W. S. ;
CORP Author State Univ. of New York Coll. of Environmental Science and Forestry, Syracuse. ;Oregon State Univ., Corvallis. Dept. of Statistics.;Corvallis Environmental Research Lab., OR.
Publisher 1990
Year Published 1990
Report Number EPA/600/A-94/074;
Stock Number PB94-174190
Additional Subjects Variance(Statistics) ; Estimators ; Population(Statistics) ; Sampling ; Environmental surveys ; Statistical distributions ; Computerized simulation ; Statistical inference ; Bivariate analysis ; Probability theory ; Correlation ; Confidence limits ; Central tendency ; Precision ; Streams ; Reprints ; RMSE(Root mean square error) ; Population space analysis ; NSWS(National Surface Water Surveys) ; Contour plots ; EMAP(Environmental Monitoring and Assessment Program)
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NTIS  PB94-174190 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 09/01/1994
Collation 10p
Abstract
The variance and two estimators of variance of the Horvitz-Thompson estimator were studied under randomized, variable probability systematic sampling. Three bivariate distributions, representing the populations, were investigated empirically, with each distribution studied for three correlations of the response variable, y, and auxiliary variable, x. The Horvitz-Thompson and Yates-Grundy variance estimators were compared based on confidence interval coverage, root mean square error, and proportion of negative estimates. The two variance estimators performed equally well except in some high-correlation populations, where the Yates-Grundy estimator had smaller root mean square error, and the Horvitz-Thompson estimator had a few negative estimates. As expected, the gain in precision of variable probability over equal probability sampling was greatest when the correlation between x and y was high, and the gain was reduced or absent when correlations were lower. (Copyright (c) American Statistical Association, 1990.)