Variance estimation in adaptive cluster sampling

Uzma Yasmeen and Mary Thompson

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 10, 2485-2497

Abstract: In this paper, a class of variance estimator is proposed of a finite population variance under an adaptive cluster sampling design in the presence of information on an auxiliary variable. We obtain expressions for the mean square error and bias for the developed estimators and their performance is evaluated on a Poisson clustered process and a real data set. The simulation study evaluates the efficiency of the suggested estimators for an adaptive cluster sampling (ACS) design and the Isaki (1983) estimator of the variance for SRSWOR over the sample variance for SRSWOR.

Date: 2020
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DOI: 10.1080/03610926.2019.1576890

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