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AbstractVery little attention has been paid by the authors for providing improved imputation methods under correlated measurement errors (CMEs) using various sampling designs. This article addresses several power ratio imputation methods and the resulting estimators under ranked set sampling (RSS) when CMEs are present. The mean square error (MSE) is developed to assess how well the suggested estimators work under CMEs. The effectiveness of the proposed imputation methods and corresponding resultant estimators is assessed by a comprehensive simulation utilizing an artificially generated population. Further, an application of the proposed imputation methods is also provided using real data from Sweden’s municipalities consisting of the total number of seats in 1982 in municipal council taken as study variable and the number of conservative seats in 1982 in municipal council taken as auxiliary variable. The CMEs might arise due to common surveyor biases if the same surveyor collects information on both the “total number of seats in municipal council” and “the number of conservative seats in that municipal council”. The simulation and real data results show that the proposed power ratio imputation methods outperform the mean, conventional ratio, and product imputation methods. [ABSTRACT FROM AUTHOR]

In surveys, successive sampling is generally considered to study the same characteristic from one occasion to another so that the change in characteristics over time can be further studied. This paper presents a class of estimators using a novel approach to estimate population mean on two occasions under successive sampling. We have carried out numerical study and simulation study to validate the significance of the proposed class and its members. We have used an optimum unmatched proportion of each class or estimator to compute their respective mean square error during simulation, while other studies considered some fixed value for unmatched units. For large population, the suggested estimation technique is always better for survey practitioners than others. [ABSTRACT FROM AUTHOR]

Abstract.This article suggested some sampling strategies allied with Searls (1964) for mean estimation on current occasions under an optimal random non response framework using auxiliary information. The characteristics of each class or estimator under their respective strategy have been studied under the optimum replacement policy. We have gauged losses, which are carried out to show the dominance over complete response classes like Singh and Vishwakarma (2007, 2009). Appropriate recommendations have been made to the survey researchers for their real-life practical applications. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we intend to utilize the multi auxiliary information available under RSS for the imputation of missing data. The mean imputation, regression imputation methods, and power transformation imputation method are identified as special cases of the proposed imputation methods. These methods are dominated by the proposed imputation methods. The theoretical comparison provides the dominance conditions of the proposed imputation methods over their conventional counterparts. In support of the theoretical findings, a simulation study is considered over a hypothetically generated population. Furthermore, some real data examples are also provided to generalize the simulation results. [ABSTRACT FROM AUTHOR]

In survey sampling, several estimation procedures have been proffered by various prominent authors to compute the impact of measurement errors (ME) but the impact of correlated measurement errors (CME) has been computed only by Shalabh and Tsai [Ratio and product methods of estimation of population mean in the presence of correlated measurement errors. Commun Stat Simul Comput. 2016;46(7):5566–5593]. This study provides a novel approach to compute the impact of CME through some logarithmic-type estimators using simple random sampling (SRS). The properties of the proffered estimators have been studied and compared with the properties of the conventional estimators. A numerical study and a broad spectrum simulation study are accomplished over real and artificially generated populations to support the theoretical results. [ABSTRACT FROM AUTHOR]

The essence of this paper is to propose some efficient combined and separate classes of estimators for estimating population mean Y ¯ under stratified simple random sampling. The bias and mean square error of the proposed classes of estimators are obtained. The proposed estimators are theoretically justified over the conventional mean estimator, classical ratio and regression estimators, Kadilar and Cingi estimators, Shabbir and Gupta estimators, Singh and Vishwakarma estimators, Koyuncu and Kadilar estimators, Singh and Solanki estimator, Yadav et al. estimator, Solanki and Singh estimator and Shahzad et al. estimator. The theoretical findings are extended with a simulation study accomplished over some artificially generated symmetric and asymmetric populations. [ABSTRACT FROM AUTHOR]

This paper proposes some optimal classes of estimators under ranked set sampling. We have acquainted some modifications of the difference and ratio type estimators under ranked set sampling, considered by various authors like Hansen et al., Srivastava, and Walsh in simple random sampling. We have demonstrated that the efficiency of the proposed optimal classes of estimators are always better than the other existing estimators. The theoretical results have been supported by a simulation study carried out over an artificially generated population. [ABSTRACT FROM AUTHOR]

This paper introduces some new ratio- and difference-type estimators of the population variance in the presence of random non-response based on Searls (The utilization of a known coefficient of variation in the estimation procedure. J Am Stat Asso. 1964;59:1225–1226) philosophy. The proposed ratio- and difference-type estimators remain better than the estimators obtained by Ahmeda et al. (Estimation of finite population variance in presence of random non-response using auxiliary variables. Infr Mang Sci. 2005;16(2):73–82) in the presence of random non-response using auxiliary variables. The properties (bias and mean square error) of the proposed estimators presented were derived up to the first-order approximation using the Taylor series approach. Conditions for which the new estimators more efficient than other estimators considered in the study were also established. Numerical examples were conducted, and the results revealed that the proposed class of estimators is more efficient than existing estimators. [ABSTRACT FROM AUTHOR]