Log Type Estimators of Population Mean Under Ranked Set Sampling

Abstract

This paper considers some log type and regression cum log type class of estimators under ranked set sampling. The suggested class of estimators are found to be better than most of the estimators proposed to date and equally efficient to the usual regression estimator under ranked set sampling. The theoretical findings have been furnished with a simulation study carried out over some artificially generated symmetric and asymmetric populations. Also, following McIntyre [1], Dell [2], and Dell and Clutter [3], we have investigated the effect of skewness and kurtosis over the efficiency of the proposed class of estimators.

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