الفهرس 
Contributions to ranked set sampling schemes
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Abstract
Ranked set sampling approach is considered a cost efficient alternative to simple random sampling when observations are costly or time-consuming but the ranking of the observations without actual measurement can be done relatively easily. Many authors suggested different modifications for ranked set sampling to come up with new sampling techniques including median ranked set sampling, extreme ranked set sampling, double ranked set sampling, neoteric ranked set sampling and doubleneoteric ranked set sampling are some modifications for ranked set sampling, have been proposed and have been shown to improvethe efficiency of estimation in some cases. Ranked set sampling is parametric in nature. However, several authors have used it in estimating the parameters of various distributions and have shown their estimators to be more efficient when compared to those based on simple random samples of the same size. In this thesis, we derivedthe likelihood function for the double ranked set sampling, general double ranked set sampling, neoteric ranked set sampling and double neoteric ranked set sampling and use the method of maximum likelihood estimation based on double ranked set sampling, neoteric ranked set sampling and double neoteric ranked set sampling to estimate the parameters of several probability distributions. We show that estimators based on double ranked set sampling are more efficient than those based on other schemes that are discussed in this is and all this schemes are more efficient than those based on ranked set sampling and simple random samples of the same size