A Generalized Shrinkage-type Estimator of Population Mean in Simple Random Sampling under conventional and non-conventional measures of auxiliary variables
DOI:
https://doi.org/10.62933/vd5wpm87Keywords:
Efficiency , Bias , mean squared error, Estimator , Shrinkage method, , Simple Random SamplingAbstract
In this study, a generalized shrinkage-type estimator of population mean in simple random sampling has been proposed. The proposed estimator is a combination of some of the known estimators in literature with the aim of obtaining estimators with higher efficiency. Its bias and mean squared error (MSE) have been derived using Taylor series up to the first order of approximation. The optimal MSE’s of the proposed class of estimators have been obtained. Theoretical comparison of the proposed shrinkage-type estimator has been also made with other existing related ratio estimators of the population mean using auxiliary information. The conditions under which the proposed shrinkage-type estimators perform better than the other existing estimators of population mean are given. Validation of results from both simulation and real data sets application reveals that the proposed shrinkage-type estimators performed better than some existing related ratio estimators considered in this work as they are having lower mean squared errors and higher percent relative efficiencies (PREs).
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