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Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling

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Audu A., Singh R., Khare S.

Statistics in Transition new series

2021

vol. 22

issue 2

125-142

In this paper, two modified, design-based calibration ratio-type estimators are presented. The suggested estimators were developed under stratified random sampling using information on an auxiliary variable in the form of robust statistical measures, including Gini’s mean difference, Downton’s method and probability weighted moments. The properties (biases and MSEs) of the proposed estimators are studied up to the terms of firstorder approximation by means of Taylor’s Series approximation. The theoretical results were supported by a simulation study conducted on four bivariate populations and generated using normal, chi-square, exponential and gamma populations. The results of the study indicate that the proposed calibration scheme is more precise than any of the others considered in this paper.

Variance estimation in stratified adaptive cluster sampling

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Yasmeen U., Noor-ul-Amin M., Hanif M.

Statistics in Transition new series

2022

vol. 23

issue 1

173-184

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.

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2 Statistics in Transition new series

1 Audu A.

1 Hanif M.

1 Khare S.

1 Noor-ul-Amin M.

1 Singh R.

1 Yasmeen U.

1 2022

1 2021

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Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling

Variance estimation in stratified adaptive cluster sampling