Abstract
This study suggests a new optimal family of exponential-type estimators for estimating population mean in stratified random sampling. These estimators are based on the traditional and nontraditional measures of auxiliary information. Expressions for the bias, mean square error, and minimum mean square error of the proposed estimators are derived up to first order of approximation. It is observed that proposed estimators perform better than the traditional estimators (unbiased, combined ratio, and combined regression) and other recent estimators. A real dataset is used to highlight the applicability of proposed estimators. In addition, a simulation study is carried out to assess the performance of new family as compared to other estimators.
Highlights
Nowadays, it is common practice to use the auxiliary/ancillary information to boost the efficiency of estimators in survey sampling
Having edge of this traditional information, many authors have been trying to explore new optimal estimators and families of estimators for estimating population mean under stratified random sampling
E motivation behind this article is to utilize the nontraditional information as well as the traditional information of the auxiliary variable to progress the estimation of population mean in stratified random sampling. is idea is initiated first time in this article under stratified random sampling
Summary
It is common practice to use the auxiliary/ancillary information to boost the efficiency of estimators in survey sampling. Most of the researchers only deal with the traditional information of auxiliary variable(s) such as standard deviation, coefficient of variation, coefficient of skewness, coefficient of kurtosis, and coefficient of correlation Having edge of this traditional information, many authors have been trying to explore new optimal estimators and families of estimators for estimating population mean under stratified random sampling. E motivation behind this article is to utilize the nontraditional information as well as the traditional information of the auxiliary variable to progress the estimation of population mean in stratified random sampling. Some of the above nontraditional measures such as decile mean, Hodges–Lehmann estimator, and tri-mean are robust measures Utilizing these measures, we can well cope with the extreme values/outliers in the dataset.
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