Hartley-Ross Type Unbiased Estimators of Population Mean Using Two Auxiliary Variables

Abstract

In survey sampling, it is a well-established phenomenon that the e ciency of estimators increases with proper information on auxiliary variable(s). Keeping this fact in mind, the information on two auxiliary variables was utilized to propose a family of Hartley-Ross type unbiased estimators for estimating population mean under simple random sampling without replacement. Minimum variance of the new estimators was derived up to the rst degree of approximation. Three real datasets were used to verify the e cient performance of the new family in comparison to the usual unbiased, Hartley and Ross, and other competing estimators.

Difference-Type-Exponential Estimators Based on Dual Auxiliary Information Under Simple Random Sampling

“…Several authors have used ratio, product and regression-type estimators to estimate population mean when both study and auxiliary variables are directly observable. For detail, see the following references: Kadilar and Cingi [2][3], Gupta and Shabbir [4], Grover and Kaur [5][6], Singh and Solanki [7], Haq and Shabbir [8], Shabbir et al [9], Ekpenyong and Enang [10], Khan et al [11], Solanki and Singh [12], Srisodaphol et al [13], Singh and Pal [14], Singh et al [15], Irfan et al [16][17], Javed et al [18] etc. This section gives a brief introduction of traditional estimators i.e.…”

Use of an efficient unbiased estimator for finite population mean

“…[4] used the known population parameters of the auxiliary variable in their suggested estimator for mean estimation. [5] extended the [1] estimator by using two auxiliary variables to estimate the population mean. [6,7] modified the [1] type estimator for mean estimation in simple and stratified 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].…”