Novel Log Type Class Of Estimators Under Ranked Set Sampling

This paper suggests some novel class of log type estimators for the estimation of population mean of study variable under ranked set sampling by utilizing information on population mean of auxiliary variable. The mean square error of the proposed class of estimators is obtained to the first order of approximation. We have compared the proposed class of estimators with some existing competitors under some specific conditions. The theoretical results are validated by a computational study using real and simulated data sets. On the lines of McIntyre (Aust. J. Agr. Res.3, 385-390 1952), Dell (1969) and Dell and Clutter (Biometrics28, 545-555 1972), the effect of skewness and kurtosis over the efficiency of the proposed class of estimators have also studied and reported.