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

Audu Ahmed (), Singh Rajesh () and Khare Supriya ()
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Audu Ahmed: Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria .
Singh Rajesh: Department of Statistics, Banaras Hindu University, Varanasi, 221005, India .
Khare Supriya: Department of Statistics, Banaras Hindu University, Varanasi, 221005, India .

Statistics in Transition New Series, 2021, vol. 22, issue 2, 125-142

Abstract: 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 first-order 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.

Keywords: calibration; outliers; percentage relative efficiency (PRE); stratified sampling. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:22:y:2021:i:2:p:125-142:n:6

DOI: 10.21307/stattrans-2021-019

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