CDF estimation in multistage pair ranked set sampling

Abstract

Multistage pair ranked set sampling (MSPRSS) is a rank-based design that improves statistical inference with respect to simple random sampling of the same size. It is applicable when exact measurement is difficult, but judgment raking of the potential sample units can be done fairly accurately and easily. The ranking is usually performed based on personal judgment or a concomitant variable, and need not be totally free of errors. This article deals with estimating the cumulative distribution function in MSPRSS. The proposed estimator is theoretically compared with its contenders in the literature. The findings are supported by numerical evidence from simulation, and real data in the context of body fat analysis. Finally, a cost analysis is performed to show the advantage of the estimator.

Practicality of Some Variations of Ranked Set Sampling

“…Mahdizadeh and Zamanzade (2017a) discussed the estimation of a symmetric distribution function under MSRSS. In addition, Mahdizadeh and Zamanzade (2017b) discussed the estimation of reliability under MSRSS. More recently, Mahdizadeh and Zamanzade (2019) explored the estimation of body fat by an efficient method through multistage pair RSS.…”