A Class of Population Mean Estimators in Stratified Random Sampling: A Case Study on Fine Particulate Matter in the North of Thailand

Abstract

Residents of Thailand’s upper northern have been facing hazardous air quality with the amount of fine particulate matter rising several times higher than the standards of the World Health Organization for many years which is classified as a level that severely affects public health. The dust problem is an urgent issue in Thailand that needs to be solved. Assessment of pollution data in advance can help the Thai government in planning to abolish and prevent ongoing dust problems for Thai citizens. A new class of population mean estimators is proposed under stratified random sampling. The bias and mean square error of the proposed estimators are studied using a Taylor series approximation. A simulation study and an application to air pollution data in the north of Thailand to investigate the performance of the estimators. The results from the air pollution data in the north of Thailand present that the proposed estimators offer the highest efficiency concerning others.