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Estimating population coefficient of variation using a single auxiliary variable in simple random sampling

100%
Singh R., Mishra M.
Statistics in Transition new series
|
2019
|
vol. 20
|
issue 4
89-111
EN
This paper proposes an improved estimation method for the population coefficient of variation, which uses information on a single auxiliary variable. The authors derived the expressions for the mean squared error of the proposed estimators up to the first order of approximation. It was demonstrated that the estimators proposed by the authors are more efficient than the existing ones. The results of the study were validated by both empirical and simulation studies.
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Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling

81%
Audu A., Singh R., Khare S.
Statistics in Transition new series
|
2021
|
vol. 22
|
issue 2
125-142
EN
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.
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