On use of adaptive cluster sampling for variance estimation

Shameem Alam, Javid Shabbir and Malaika Nadeem

Journal of Applied Statistics, 2025, vol. 52, issue 12, 2291-2305

Abstract: Adaptive cluster sampling is particularly helpful whenever the target population is unique, dispersed unevenly, concealed or difficult to find. In the current investigation, under an adaptive cluster sampling approach, we propose a ratio-product-logarithmic type estimator employing a single auxiliary variable for the estimation of finite population variance. The bias and mean square error of the proposed estimator are developed by using simulation as well as real data sets. The study results show that for estimating the finite population variance, the proposed estimator outperforms the competing estimators.

Date: 2025
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DOI: 10.1080/02664763.2025.2460072

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