Unbalanaced Ranked Set Sampling and its Application in Medical Science

Rank set sampling is a sampling procedure that can be considerably more efficient than simple random sampling. Although ranking processes for continuous variables that are implemented through either subjective judgment or via the use of a concomitant variable have been studies extensively in the literature, the use of RSS in the case of binary variables has not been investigated thoroughly. We use a National Health and Nutrition Examination Survey III (NHIS) data set to investigate the application of unbalanced RSS to estimation of population proportion. Our results indicate that this use of logistic regression improve the accuracy of the preliminary ranking in unbalanced rank set sampling and leads to substantial gains in precision for estimation of a population proportion. Further, we illustrate how data from one source can be used to construct the necessary logistic regression equation, which can in turn be used to estimate the relevant properties. This research was conducted to find out whether unbalanced rank set sampling is better than the simple random sampling, balanced rank set sample and rank set sampling. We also find out whether risk factors of diabetes like age, pregnancies, pg concentration, diastolic BP, trifold thick, serum Ins. After using simple random sampling, than we rank the values, using balanced and unbalanced rank set sampling.