Improved Estimator of Finite Population Variance Using Coefficient of Quartile Deviation

Abstract

This study introduces a new, better, class of ratio estimators for the estimation of population variance of the study variable by using the coefficient of quartile deviation of auxiliary variable. Bias and mean square error of the proposed class of estimators are also derived. The conditions of efficiency comparison are also obtained. Simulation and different secondary data sets are used to evaluate the efficiency of proposed class of variance estimators over existing class of estimators. The empirical study shows that the suggested class of estimators is more efficient the existing class of estimators for the population variance.

Estimation of Heterogeneous Population Variance using Memory-type Estimators based on EWMA statistic in the presence of Measurement Error for Time-Scaled Surveys

“…In survey sampling, Noor-ul-Amin (2019) used EWMA statistic for the estimation of population mean using memory-type ratio and product estimators for time-scaled surveys which significantly improved the results. The estimators for estimating population variance received significant attention by various authors such as; Isaki (1983), Gupta et al (1992), Jhajj et al (2005), Shabbir and Gupta (2007), Yadav and Kadilar (2013), Adichwal et al (2017), Javed et al (2018), Ismail et al (2018), Qureshi et al (2019aQureshi et al ( , 2019b, Yadav et al (2019), , Kaleem et al (2020), Naz et al (2020) and Niaz et al (2021). Stratified sampling is considered to be a useful scheme for parameter estimation of heterogeneous population to increase the precision over the simple random sampling design.…”

Bayesian Estimation for the Difference between Coefficients of Quartile Variation of Delta-Lognormal Distributions: An Application to Rainfall in Thailand

“…Ambati et al [17] introduced ratio-and regression-type estimators for estimating the CQV in finite populations. Javed et al [18] proposed a class of ratio estimators for estimating population variance, utilizing the CQV of an auxiliary variable. Altunkaynak and Gamgam [19] recommended the bootstrap method to establish confidence intervals for the CQV in non-normal distributions.…”

Memory-type variance estimators using exponentially weighted moving average statistic in presence of measurement error for time-scaled surveys

“…The estimation of variance is important when the variability control is difficult in its application such as in agricultural research, experiments in biosciences, manufacturing industries, and pharmaceutical laboratories. The estimation of population variance in the study variable also received significant interest from various authors such as [1][2][3][4][5][6][7][8][9][10][11].…”