Variance Estimation in the Presence of Measurement Errors Under Stratified Random Sampling

Abstract

This study focuses on the estimation of population variance of study variable in stratified random sampling using auxiliary information when the observations are contaminated by measurement errors. Three classes of estimators of variance under measurement error are proposed by using the approach of Srivastava and Jhajj [18] for the study variable. The properties of the estimator viz. bias and mean square error of the proposed classes of estimators are provided. The conditions for which proposed estimators are more efficient compared to usual estimators are discussed. It is shown that the proposed classes of estimators include a large number of estimators of the population variance of stratified random sampling and their bias and mean square error can be easily derived.