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ON COMPARISON OF PERFORMANCES OF SYNTHETIC AND NON-SYNTHETIC GENERALIZED REGRESSION ESTIMATIONS FOR ESTIMATING LOCALIZED ELEMENTS  

SARA AMITAVA (Directorate General of Mines Safety, Dhanbad, Jharkhand)
Publication Information
Journal of the Korean Statistical Society / v.34, no.1, 2005 , pp. 73-83 More about this Journal
Abstract
Thompson's (1990) adaptive cluster sampling is a promising sampling technique to ensure effective representation of rare or localized population units in the sample. We consider the problem of simultaneous estimation of the numbers of earners through a number of rural unorganized industries of which some are concentrated in specific geographic locations and demonstrate how the performance of a conventional Rao-Hartley-Cochran (RHC, 1962) estimator can be improved upon by using auxiliary information in the form of generalized regression (greg) estimators and then how further improvements are also possible to achieve by adopting adaptive cluster sampling.
Keywords
Adaptive cluster sampling; Generalized regression estimator; Mean square error; Synthetic estimator; Rao-Hartley-Cochran sampling scheme;
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