Robust Ratio Estimation with an Application to Covid-19 Data from Louisiana
Azaz Ahmed
Department of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.
Muhammad Hanif
Department of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.
Evrim Oral *
LSU Health Sciences Center, School of Public Health, Biostatistics Program, New Orleans, USA.
*Author to whom correspondence should be addressed.
Abstract
Traditional ratio estimator loses its efficiency when there are outliers in the data or when the error term is not normally distributed. Specifically in health-related data, many biological processes can be modeled by Laplace distribution. We propose a novel robust ratio estimator that utilizes Lloyd’s estimator for the cases where the error term is from the Laplace distribution. We derive the mean square error of the proposed estimator and compare it with some other existing estimators using extensive simulations. We use the proposed estimator to estimate Covid-19 cases and deaths in Louisiana and demonstrate its performance.
Keywords: Auxiliary variable, Covid-19, generalized least square estimator, modified maximum likelihood, robust ratio estimator