MODIFIED REGRESSION APPROACH IN PREDICTION OF FINITE POPULATION MEAN USING KNOWN COEFFICIENT OF VARIATION
Keywords:
Regression estimator, Prediction, Predictor, Coefficient of variation, Bias, Mean square error, Auxiliary variable, Relative efficiency, Simple random sampling, Effective sample sizeAbstract
In this paper, we are utilizing the modified regression approach for the prediction of finite population mean, with known coefficient of variation of study variable y , under simple random sampling without replacement. The bias and mean square error of the proposed estimator are obtained and compared with the usual regression estimator of the population mean and comes out to be more efficient in the sense of having lesser mean square error. The optimum class of estimators is obtained and for the greater practical utility proposed optimum estimator based on estimated optimum value of the characterizing scalar is also obtained and is shown to retain the same efficiency to the first order of approximation as the former one. A numerical illustration is also given to support the theoretical conclusions.
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