A Chain Ratio Exponential-Type Compromised Imputation for Mean Estimation: Case Study on Ozone Pollution in Saraburi, Thailand

Abstract

Due to its impact on health and quality of life, Thailand’s ozone pollution has become a major concern among public health investigators. Saraburi Province is one of the areas with high air pollution levels in Thailand as it is an important industrialized area in the country. Unfortunately, the August 2018 Pollution Control Department (PCD) report contained some missing values of the ozone concentrations in Saraburi Province. Missing data can significantly affect the data analysis process. We need to deal with missing data in a proper way before analysis using standard statistical techniques. In the presence of missing data, we focus on estimating ozone mean using an improved compromised imputation method that utilizes chain ratio exponential technique. Expressions for bias and mean square error (MSE) of an estimator obtained from the proposed imputation method are derived by Taylor series method. Theoretical finding is studied to compare the performance of the proposed estimator with existing estimators on the basis of MSE’s estimators. In this case study, the results in terms of the percent relative efficiencies indicate that the proposed estimator is the best under certain conditions, and it is then applied to the ozone mean estimation for Saraburi Province in August 2018.

New Ratio Estimators for Population Mean under Unequal Probability Sampling Without Replacement in the Presence of Missing Data: A Case Study on Fine Particulate Matter in Bangkok, Thailand

“…The results indicated that the average PM2.5 was 48.20 μg/m 3 with MSE equal to 0.90 μg/m 3 . Chodjuntug and Lawson [19] suggested using a response rate which was free from known parameters and therefore easy to use and also a constant to minimize MSE to estimate the PM2.5 at Kanchana Phisek Road in Bangkok. The Chodjuntug and Lawson estimator [19] was in the form of a regression and exponential estimator under SRSWOR.…”

“…Chodjuntug and Lawson [19] suggested using a response rate which was free from known parameters and therefore easy to use and also a constant to minimize MSE to estimate the PM2.5 at Kanchana Phisek Road in Bangkok. The Chodjuntug and Lawson estimator [19] was in the form of a regression and exponential estimator under SRSWOR. The results showed that the mean PM2.5 from their suggested estimator was 42.22 μg/m 3 with MSE of 0.34 μg/m 3 .…”

“…The Lawson estimator used the response rate, a sample regression coefficient and an optimum constant in the estimator which yielded better results than the existing estimator, producing a lower mean square error. Similar to Chodjuntug and Lawson [19], Lawson [20] also suggested the use of response rate and sample regression coefficient in the estimator so when there were no unknown auxiliary parameters the Lawson estimator could still be applied to estimate the population mean with missing data.…”