Efficient Generalized Ratio-Product Type Estimators for Finite Population Mean with Ranked Set Sampling
V. L. Mandowara, Nitu Mehta
Abstract
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V. L. Mandowara, Nitu Mehta
Abstract
Open-access reader
In this paper we suggest two modified estimators of the population mean using the power transformation based on ranked set sampling (RSS). The first order approximation of the bias and of the mean squared error of the proposed estimators are obtained. A generalized version of the suggested estimators by applying the power transformation is also presented. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in simple random sampling (SRS). A numerical illustration is also carried out to demonstrate the merits of the proposed estimators using RSS over the usual estimators in SRS.
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In this paper we suggest two modified estimators of the population mean using the power transformation based on ranked set sampling (RSS). The first order approximation of the bias and of the mean squared error of the proposed estimators are obtained. A generalized version of the suggested estimators by applying the power transformation is also presented. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in simple random sampling (SRS). A numerical illustration is also carried out to demonstrate the merits of the proposed estimators using RSS over the usual estimators in SRS.
Key concepts: Estimator, RSS, Mathematics, Extremum estimator, Simple random sample, Population mean, Mean squared error, Bootstrapping (finance)