Improved Ratio Type Estimators using some Prior Information in Sample Surveys: A Case Study of Fine Particulate Matter in Thailand

Abstract

Air pollution affects Thai people's health and social life nowadays as it exceeds the standards levels of both Thailand and the World Health Organization. Estimating air pollution data can benefit understanding and determining policies to help deal with this issue. Prior knowledge from past surveys or censuses could be useful for increasing the effect of the estimation. Improved ratio estimators utilizing prior knowledge in simple random sampling without replacement have been advocated. The property of the mean square error of the proposed class of estimators is obtained. We applied the proposed estimators to the fine particulate matter data in Dindang in 2019. The results from the air pollution data illustrate the improved ratio type estimators work better with respect to the existing estimator using some prior information. Existing knowledge of the quartile average and the median of the auxiliary variable gives rise to the best estimators with the lowest mean square errors for estimating fine particulate matter. Nevertheless, the proposed estimators are useful for small sampling fractions which can help in financial and time-consuming.