Ganes S. Ganesalingam
Massey University-Palmerston North
Ranked Set Sampling vs Simple Random Sampling in the Estimation of the Mean and Ratio
Ganes S. Ganesalingam and Siva Ganesh
S.Ganesalingam@massey.ac.nz and S.firstname.lastname@example.org
It is common in practice that the variable of interest of the experimental units can be ranked easily than quantification. In such situations the ranked set sampling is more beneficial and cost effective.
Ranked set sampling is a novel method of achieving observational economy when compared to the traditional simple random sampling in the context of estimation of parameters such as population mean, population ratio, population total and others. Ranked set sampling employs judgement ordering to obtain the actual sample and hence yield a sample of observations that are more representative of the underlying population. Therefore, either greater confidence is gained for a fixed number of observations, or for a desired level of confidence less number of observations are needed. In this paper we introduce the basic concept of the ranked set sampling and its application in the estimation of population mean and population ratio using a real data set on body measurements.
Keywords: Ranked set sample (RSS); simple random sample (SRS); Population ratio; Population mean; Relative precision; Relative saving; Empirical Efficiency.