Improved estimator of population mean under compromise method of imputation

Abstract

Analysing data becomes complicated when multiple values are missing, as missing data introduces bias, hampers handling and analysis, and decreases efficiency. Imputation is a technique used to address this issue by filling in missing values based on existing data and auxiliary information. Various methods are available for imputation. Once the missing observations are imputed, the complete data set can be analysed using traditional methods. Historically, methods such as Mean Imputation, Ratio Imputation, and Compromise Imputation have been employed to address missing data. In this study an alternate estimator of population mean using estimator given by Khoshnevisan (2007) under compromised imputation method is given. The expression for bias and mean squared error (MSE) up to the first order approximation are derived. An empherical study is also carried out in order to compare the efficiency of the estimator with the previously existing estimators.