Estimation of Distribution Function Based on Ranked Set Sampling: Missing Data Approach
In many studies, it is a well-established fact that estimation under either ranked set sample (RSS) or its variations is much better than other popular sampling techniques. In this article, missing data approach is adopted to present new estimators for distribution function under RSS setup. Using EM algorithm and linear interpolation technique, new cumulative distribution function (CDF) estimators are proposed. The consistency of the new estimators is analytically discussed. It merges from Monte Carlo simulations that the proposed estimators have a good performance as compared with their competitors based on simulated as well as empirical data set.