Optimal Imputation Methods under Stratified Ranked Set Sampling

Abstract

It is long familiar that the stratified ranked set sampling (SRSS) is more efficient than ranked set sampling (RSS) and stratified random sampling (StRS). The existence of missing values alter the final inference of any study. This paper is fundamental effort to suggest some combined and separate imputation methods in presence of missing data under SRSS. It has been shown that the proposed imputation methods become superior than the mean imputation method, ratio imputation method, Diana and Perri (2010) type imputation method and Sohail et al. (2018) type imputation methods. A simulation study is administered over two hypothetically drawn asymmetric populations.