Joint Parameter Estimation in Measurement Errors and Non-Response for Sensitive Variables

In this paper, joint parameter estimation for mean and variance was considered in the presence of measurement errors and non-response for the Sensitive variable using auxiliary information. The properties of the suggested estimator(s) have been studied. Expressions for bias and mean square error up to first order of approximation are derived, and the theoretical characteristics of the suggested estimators are scrutinised. A numerical study is carried out to observe the performance of the proposed estimators. The scope of this work is to create better estimators that can effectively manage both kinds of mistakes at the same time, especially in randomised response settings where biasing sensitive data is common. Applications to actual data and simulation studies are used to evaluate the estimators' effectiveness. When both measurement error and non-response are present, the results show that the suggested estimators outperform the traditional estimators.