Enhancing estimation efficiency with proposed estimator: A comparative analysis of Poisson regression-based mean estimators

In many studies, the Poisson regression model is mostly intended for modelling count responses. Recently, it was shown that the exploitation of the Poisson regression coefficient within the Ko & ccedil; (2021) ratio estimator increases the efficiency of the estimator. This study uses a new Poisson regression-based regression-type mean estimator with simple random sampling and finds its related mean square error formula. Essentially, we contrast the suggested estimators' mean square errors with those of previously published estimators. For the real data study, estimators were calculated for three real populations and the superior performance of the proposed estimator was observed. Similar results were obtained from the simulation study. As an outcome of these estimations, the proposed estimators are more effective than existing estimators. The empirical results verified the theoretical results to be remarkable.