Variance estimation in stratified adaptive cluster sampling

Yasmeen Uzma (), Noor-ul-Amin Muhammad () and Hanif Muhammad
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Yasmeen Uzma: University of Waterloo, Canada . The University of Lahore, Pakistan .
Noor-ul-Amin Muhammad: COMSATS Institute of Information Technology, Lahore. Pakistan .
Hanif Muhammad: National College of Business Administration and Economics, Pakistan .

Statistics in Transition New Series, 2022, vol. 23, issue 1, 173-184

Abstract: In many sampling surveys, the use of auxiliary information at either the design or estimation stage, or at both these stages is usual practice. Auxiliary information is commonly used to obtain improved designs and to achieve a high level of precision in the estimation of population density. Adaptive cluster sampling (ACS) was proposed to observe rare units with the purpose of obtaining highly precise estimations of rare and specially clustered populations in terms of least variances of the estimators. This sampling design proved to be more precise than its more conventional counterparts, including simple random sampling (SRS), stratified sampling, etc. In this paper, a generalised estimator is anticipated for a finite population variance with the use of information of an auxiliary variable under stratified adaptive cluster sampling (SACS). The bias and mean square error expressions of the recommended estimators are derived up to the first degree of approximation. A simulation study showed that the proposed estimators have the least estimated mean square error under the SACS technique in comparison to variance estimators in stratified sampling.

Keywords: variance estimator; stratified sampling; stratified adaptive cluster sampling (SACS) (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:23:y:2022:i:1:p:173-184:n:9

DOI: 10.2478/stattrans-2022-0010

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