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One eminent disadvantage of many existing optimal estimators/class of estimators is that they are typically biased. In this article, we proposed an optimum class of unbiased estimators for estimating the population mean under simple random sampling without replacement (SRSWOR) scheme. Proposed class is a blend of three concepts: 1) information on auxiliary variable, 2) the ranks of auxiliary variable and 3) Hartley-Ross type unbiased estimation procedure. Expressions for the bias and the minimum variance of the new class are derived up to first degree of approximation. To highlight the application of proposed class, five real data sets are used. Numerical findings confirm that the new class behaves efficiently as compared to traditional unbiased estimator and other almost unbiased estimators under study. In addition, Monte Carlo simulation study is conducted through two real populations to assess the performance of proposed class against competitors. On the basis of theoretical and numerical findings, it is concluded that new proposed class can generate optimum unbiased estimators under SRSWOR scheme. Therefore, use of proposed class is recommended for future applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, we propose a general family of exponential-type estimators for enhanced estimation of population mean in simple random sampling. These estimators are based on the available parameters of the auxiliary variable such as coefficient of skewness, coefficient of kurtosis, standard deviation and coefficient of variation etc. Expressions for bias, mean squared error and minimum mean squared error of the proposed family are derived up to first degree of approximation. Five natural populations are considered to assess the performance of the proposed estimators. Numerical findings confirm that the proposed estimators dominate over the existing estimators such as sample mean, ratio, regression, Singh et al. (2008, 2009), Upadhyaya et al. (2011), Yadav and Kadilar (2013) and Kadilar (2016) in terms of mean squared error. [ABSTRACT FROM AUTHOR]

The present work suggests some difference-cumexponential ratio-type estimators to deal with the problem of estimation for population mean. The suggested estimators are based on the linear combination of two auxiliary variables under simple and stratified random sampling schemes. Expressions for the bias, mean squared error (MSE) and minimum MSE of the suggested estimators are derived up to the first degree of approximation. Different real life datasets are used to show the superiorities in terms of percent relative efficiencies (PREs) of the new estimators. The suggested estimators are more efficient as they provide maximum gain in PREs as compared to the traditional and competing estimators under study. [ABSTRACT FROM AUTHOR]

Most of the research work in the theory of survey sampling only deals with the sampling errors under the assumptions: (i) there is a complete response and (ii) recorded information from individuals is correct but in practice it is not always true. Non-sampling errors like non-response and measurement errors (MEs) mostly creep into the survey and become more influential for estimators than sampling errors. Considering this practical situation of non-response and MEs jointly, we proposed an optimum class of estimators for population mean under simple random sampling using conventional and non-conventional measures. Bias and mean square error of the proposed estimators are derived up to first degree of approximation. Moreover, a simulation study is conducted to assess the performance of new estimators which proves that proposed estimators are more efficient than the traditional Hansen and Hurwitz estimator and other competing estimators. [ABSTRACT FROM AUTHOR]