2013•Journal of Reliability and Statistical StudiesRequires access

DUAL TO RATIO AND PRODUCT TYPE EXPONENTIAL ESTIMATORS IN STRATIFIED RANDOM SAMPLING USING TWO AUXILIARY VARIATES

Rajesh Tailor, Narendra Kumar Jatwa, Ritesh Tailor, Neha Garg

Open publisher page 7 citations

Abstract

This paper discusses the problem of estimation of population mean in stratified random sampling. In fact, in this paper, dual to ratio and product type exponential estimators in stratified random sampling have been suggested. The biases and mean squared errors of the suggested estimators haven been obtained up to the first degree of approximation. The suggested estimators have been compared with existing estimators and conditions under which suggested estimators are more efficient than other considered estimators have been obtained. An empirical study has been carried out to demonstrate the performance of the suggested estimators.

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What this paper is about

This paper discusses the problem of estimation of population mean in stratified random sampling. In fact, in this paper, dual to ratio and product type exponential estimators in stratified random sampling have been suggested. The biases and mean squared errors of the suggested estimators haven been obtained up to the first degree of approximation. The suggested estimators have been compared with existing estimators and conditions under which suggested estimators are more efficient than other considered estimators have been obtained. An empirical study has been carried out to demonstrate the performance of the suggested estimators.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper discusses the problem of estimation of population mean in stratified random sampling. In fact, in this paper, dual to ratio and product type exponential estimators in stratified random sampling have been suggested. The biases and mean squared errors of the suggested estimators haven been obtained up to the first degree of approximation. The suggested estimators have been compared with existing estimators and conditions under which suggested estimators are more efficient than other considered estimators have been obtained. An empirical study has been carried out to demonstrate the performance of the suggested estimators.

Key concepts: Estimator, Stratified sampling, Mathematics, Statistics, Extremum estimator, M-estimator, Sampling (signal processing), Bootstrapping (finance)

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