A new estimator with theoretical insights and simulation: Its empirical evaluation in community governance and radiation sector

This paper suggest a new class of estimators for estimation of population proportion using two auxiliary attributes that has been produced to overcome the issue of inferential problems in an empirical setting characterized by structural complexity. The estimator has a strong theoretical foundation; the basic statistical properties of the estimator, such as bias, mean squared error and percentage relative efficiency have their formal derivation. In order to supplement the theoretical discussion, a comprehensive Monte Carlo simulation study is undertaken to investigate the performance of the estimator in finite sample in a wide variety of data generating models. The practical usefulness of the proposed class of estimators is evidenced by it being used in community governance and the radiation science data. The performance of the estimator is put in a systematic comparison with the adopted existing estimators. The empirical findings indicate that the proposed estimator has high robustness and efficiency at least in the environments, which have a complex governance structure and regulate it. This paper brings a methodologically sound estimation technique to the community because of the theoretical soundness, validation via simulation and application in practice. The suggested estimator holds a great potential of informed policy analysis and evidence-based decision-making in the contexts of governance and radiation-related environments, not to mention that it also enables the opportunity to carry out further methodology extensions in the future.