The improved estimates of population proportion using auxiliary attributes: Application in radiation science

Abstract

The estimation of population proportions plays an important role in the scientific work, especially in radiation science, where evaluations are frequently implemented as estimate of the frequency of outcomes. This article presents new improved class of estimators of population proportion estimation under simple random sampling. The suggested class supports a diverse variety of estimators all of which can be customized to various data structures and situations, which leads to greater flexibility and precision. To gain some analytical understanding of estimator performance we find expressions for the bias and mean squared error up-to the first order of approximation. The radiation datasets and a simulation study used to assess the estimators efficiency. The MSE criterion is used to determine a comparative analysis with conventional and preliminary estimators. The results show that the recommended estimators have minimum values of MSE which means they are more efficient. This work also reveals the best conditions within which the estimators can work most efficiently as they produce the least MSE. In general this study provides a flexible estimation procedure at a more dependable base of estimating proportions in complicated sampling strategies. The generalized estimators have a great vision of being used in radiation science, and it plays a pivotal role in estimation of important health and exposure parameters like epidemiological hazardous verses, assessment of risks, and policies.