Robust Ratio Estimation with an Application to Covid-19 Data from Louisiana

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 Lloy...

Full description

Saved in:
Bibliographic Details
Published in:Journal of Advances in Mathematics and Computer Science 2023-07, Vol.38 (9), p.65-80
Main Authors: Ahmed, Azaz, Hanif, Muhammad, Oral, Evrim
Format: Article
Language:English
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2456-9968
2456-9968
DOI:10.9734/jamcs/2023/v38i91805