In the present time, a large number of modified estimators have been proposed by authors to obtain efficiency. In this study, we suggested an alternative regression type estimator for estimating finite population mean...In the present time, a large number of modified estimators have been proposed by authors to obtain efficiency. In this study, we suggested an alternative regression type estimator for estimating finite population mean</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> when there is either </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">positive or negative correlation between study variables and auxiliary variables. We obtained bias and mean square error equation of the proposed estimator ignoring the first</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">order approximation and found the theoretical conditions that make proposed estimator more efficient than simple random sampling mean estimator, product estimator and ratio estimator. In addition, these conditions are supported by a numerical example and it has been concluded that the proposed estimator performed better comparing with the usual simple random sampling mean estimator, ratio estimator and product estimator.展开更多
文摘In the present time, a large number of modified estimators have been proposed by authors to obtain efficiency. In this study, we suggested an alternative regression type estimator for estimating finite population mean</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> when there is either </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">positive or negative correlation between study variables and auxiliary variables. We obtained bias and mean square error equation of the proposed estimator ignoring the first</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">order approximation and found the theoretical conditions that make proposed estimator more efficient than simple random sampling mean estimator, product estimator and ratio estimator. In addition, these conditions are supported by a numerical example and it has been concluded that the proposed estimator performed better comparing with the usual simple random sampling mean estimator, ratio estimator and product estimator.
