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This article suggests the class of estimators of population mean of study variable using various parameters related to an auxiliary variable with its properties in simple random sampling. It has been identified that the some existing estimator/classes of estimators are members of suggested class. It has been found theoretically as well as empirically that the suggested class is better than the existing methods. [ABSTRACT FROM AUTHOR]

This paper suggested an alternative ratio-cum-product type class of estimators of population mean using exponentiation method in simple random sampling. Approximate bias and mean squared error formulae of the suggested class of estimators have been obtained up to the first order of approximation. Asymptotic optimum estimator in the suggested class of estimators has been obtained with its mean squared error formula. Regions of preferences have been obtained under which the suggested class of estimators has been better than the usual unbiased, ratio and product estimators and the estimators according to Singh and Agnihotri (2008). Some examples are cited with numerical study. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we propose an improved estimator of finite population mean using information on two auxiliary attributes under simple random sampling (SRS) and two-phase sampling schemes. The bias and mean squared error (MSE) expressions of the proposed estimators are obtained up to first order of approximation. It is shown theoretically, that the proposed estimator is more efficient than the ratio-product type estimator, regression estimator, difference type estimator and Malik and Singh (2013) [13] estimator in both SRS and two phase sampling. An empirical study using two data sets is also conducted to support the theoretical findings. [Copyright &y& Elsevier]

Abstract: We propose a two parameter ratio-product-ratio estimator for estimating a finite population variance based on simple random sampling without replacement. The bias and mean square error of the proposed estimator are obtained to the first degree of approximation. We have derived the conditions for the parameters under which the proposed estimator has smaller mean square error than the sample variance, ratio and product estimators. We carry out an application showing that the proposed estimator outperforms the traditional estimators using different data sets. [Copyright &y& Elsevier]

This article examines a methodology to improve the efficiency of surveys to locate and estimate characteristics of rare populations. Two multiplicity counting rules are compared with a conventional rule in terms of reporting errors, sample variances, and survey yields. The analysis is based on data from two survey field experiments with diagnosed cancer patients and a subsample of their siblings or children who reside outside their households. The article concludes with a brief discussion of the circumstances in which a multiplicity counting rule is the preferred methodology and those in which other approaches should be pursued. [ABSTRACT FROM AUTHOR]