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

Abstract

Recently, Haq et al. [A new estimator of finite population mean based on the dual use of the auxiliary information. Commun Stat Theory Methods. 2017;46(9):4425-4436] utilized the dual auxiliary information under simple random sampling only. Motivated by their idea, we initiated the dual use of auxiliary variable under a stratified random sampling scheme. Dual use of auxiliary variable consists: (1) the original auxiliary information and (2) the ranked auxiliary information. We proposed new optimal exponential-type estimators for the estimation of the finite population mean. Mathematical properties such as bias and mean squared error of the proposed estimators are derived. Monte Carlo simulation studies are included to successfully validate the theoretical results. Moreover, the applicability of the proposed estimators is highlighted through empirical interpretation with the help of a real-life data set. It is clearly identified from the numerical results that our proposed estimators are more efficient over the competitors. ARTICLE HISTORY

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

“…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. Yadav and Tailor [ 9 ] estimated a finite population mean using two auxiliary varaibles under stratified random sampling.…”

A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random 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. 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].…”

A new estimator and approach for estimating the subpopulation parameters

“…Sampling methods are essential part of many research studies on a population [1][2][3][4]. However, sometimes study of a population division will become of resear cher's interest.…”