A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling

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.

A simulation study: Using dual ancillary variable to estimate population mean under stratified random sampling

“…Mradula et al [ 6 ] obtained an efficient estimation of population mean under stratified random smapling with linear cost function. Javed et al [ 7 ] proposed a simulation based study for progressive estimation of population mean through traditional and non-traditional measures in stratified random sampling. Javed and Irfan [ 8 ] obtained a simulation based on new optimal estimators for population mean by using the dual auxiliary information in stratified random sampling.…”