Ratio Estimator of Population Mean in Simple Random Sampli
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Date
2022-11-02
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Kenyatta University
Abstract
This study considers the problem of estimating the population mean in Simple Random Sampling. One key objective of any statistical estimation process is to find estimates of parameter of interest with more efficiency. Incorporating additional information in the estimation procedure yields enhanced estimators. Ratio estimation improves accuracy of the estimate of the population mean by incorporating prior information of a supporting variable that is highly associated with the main variable. This study incorporates non-conventional measure (Tri-mean) with quartile deviation as they are not affected by outliers together with kurtosis coefficients and information on the sample size to develop an estimator with more precision. Using Taylor series expansion, the properties of the estimator are evaluated to first degree order. Further, the estimator's properties are assessed by bias and mean squared error. Efficiency conditions are derived theoretically whereby the suggested estimator performs better than the prevailing estimators. To support the theoretical results, simulation and numerical studies are undertaken to assess efficiency of the suggested estimator over the existing estimators. Percentage relative efficiency indicate the suggested estimator performs better compared to the existing estimators. It is concluded that the suggested estimator is more efficient than the existing estimators.
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Supervisors:
1. Leo Odongo