An Efficient Class of Estimators for the Population Mean Using Auxiliary Information in Stratified Random Sampling

Abstract

This article addresses the problem of estimating the population mean in stratified random sampling using the information of an auxiliary variable. A class of estimators for population mean is defined with its properties under large sample approximation. In particular, various classes of estimators are identified as particular member of the suggested class. It has been shown that the proposed class of estimators is better than usual unbiased estimator, usual combined ratio estimator, usual product estimator, usual regression estimator and Koyuncu and Kadilar (2009) class of estimators. The results have been illustrated through an empirical study.

A simulation study: new optimal estimators for population mean by using dual auxiliary information in stratified random sampling

“…Both real-life applications and simulation studies are performed to access the potentiality of the proposed estimators over the competitors. Numerical findings confirmed that the proposed estimators have the minimum mean square errors than all the other existing estimators such as usual unbiased, combined ratio, combined regression, Haq and Shabbir [7], Singh and Solanki [8] and Solanki and Singh [10,11]. Therefore, new proposed estimators under stratified random sampling are very attractive to the survey statisticians.…”

“…In this section, we compare the performance of newly proposed estimators with the traditional unbiased, combined ratio and combined regression estimators and existing estimators, i.e. Haq and Shabbir [7], Singh and Solanki [8] and Solanki and Singh [10,11]. We considered a real-life data set of Turkey (2007) used by Koyuncu and Kadilar [3].…”

“…It is a powerful and flexible method that is widely used in practice. Many researchers, such as Kadilar and Cingi [1,2], Koyuncu and Kadilar [3,4], Singh and Vishwakarma [5], Shabbir and Gupta [6], Haq and Shabbir [7], Singh and Solanki [8], Yadav et al [9], Solanki and Singh [10,11], Aslam [12], Bhatti et al [13], Javed et al [14], Marin et al [15][16][17], etc. have contributed to estimate the finite population mean under stratified random sampling scheme.…”

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

“…(34) 3.7. Solanki and Singh [10]. Given below is the class of estimators suggested by Solanki and Singh [10]:…”

“…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. Stratified random sampling has often proved needful in improving the precision of estimators over simple random sampling, for instance, see works of Kadilar and Cingi [1,2], Koyuncu and Kadilar [3,4], Singh and Vishwakarma [5][6][7], Shabbir and Gupta [8], Haq and Shabbir [9], Singh and Solanki [10], Yadav et al [11], Solanki and Singh [10,12], Javed et al [13], and Javed and Irfan [14].…”