PERFORMANCE EVALLUATION OF IMPUTATION-BASED ESTIMATORS FOR NON-RESPONSE AND MEASUREMENT ERROR CHALLENGES
Abstract
This study aims to develop a robust class of estimators designed to address non-response and measurement erros, which frequently complicate data collection in mdeical and social science research. By employing call-back and imputation schemes, the proposed estimators enhance efficiency and accuracy. We derived properties such as bias and mean squared error using Taylor's series explansion and testes their consistenct. An empirical study with simulated data rfom vaious distributions revealed that the proposed estimators outperform existing ones. Thus, these modified classes are recommended for practical application in data analysis, especially in the presence of non-reponse and measurement errors.
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