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This paper investigates the performance of combined and separate log type class of estimators of population mean under stratified ranked set sampling. The expressions of bias and mean square error of the proposed estimators are deduced. The theoretical comparison of the proposed estimators with the existing estimators is carried out and the efficiency conditions are reported. The credibility of theoretical results is extended by a simulation study conducted over various artificially generated symmetric and asymmetric populations. The results of the simulation study show that the proposed class of estimators dominate the well-known existing estimators. [ABSTRACT FROM AUTHOR]

In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study. [ABSTRACT FROM AUTHOR]