Imputation of missing data using multi auxiliary information under ranked set sampling

Abstract

In this paper, we intend to utilize the multi auxiliary information available under RSS for the imputation of missing data. The mean imputation, regression imputation methods, and power transformation imputation method are identified as special cases of the proposed imputation methods. These methods are dominated by the proposed imputation methods. The theoretical comparison provides the dominance conditions of the proposed imputation methods over their conventional counterparts. In support of the theoretical findings, a simulation study is considered over a hypothetically generated population. Furthermore, some real data examples are also provided to generalize the simulation results.