Proportion Estimation Using Enhanced Class of Estimators Under Simple Random Sampling: Application with Real Data Sets and Simulation

ABSTRACT

This study aims to suggest a generalized class of estimators for population proportion under simple random sampling, which uses auxiliary attributes. The bias and MSEs are considered derived to the first degree approximation. The validity of the suggested and existing estimators is assessed via an empirical investigation. The performance of estimators has been evaluated using real data sets and a simulation study. We found that the suggested estimators have minimum mean squared error (MSE) and a higher percentage relative efficiency when compared to existing estimators based on real data sets and simulation studies. The suggested estimator performs better than the existing ones, according to the theoretical and empirical findings.

KEYWORDS:

• Proportion
• study attribute
• auxiliary attribute
• bias
• MSE
• PRE
• simulation

Disclosure statement

No potential conflict of interest was reported by the author(s).