Work

Advancing Coefficient of Variation Estimation Under Additive Distortion Measurement Errors: Exploring New Avenues Beyond Independence Assumptions

Year: 2026

Type: article

Abstract: ABSTRACT This paper focuses on estimating the coefficient of variation for an unobservable variable within an additive distortion measurement errors framework, where the observed variable is a sum of the unobservable variable and an unknown contaminating function of a confounding variable. We use the conditional mean calibration approach to derive a calibrated variable and introduce the calibrated coefficient of variation. Asymptotic properties of the estimators are derived, showing their efficiency. Confidence intervals for the coefficient of variation are constructed using asymptotic normal‐based and empirical likelihood‐based methods. We also propose test statistics for examining the exponential distribution of the unobservable variable. To relax the independence assumption between the confounding and unobservable variables, two novel identifiability conditions are introduced. Simulation results and a real‐world dataset analysis validate the effectiveness and robustness of our approach.

Source: Statistical Analysis and Data Mining The ASA Data Science Journal

DOI: 10.1002/sam.70058

Authors: Junbang Zhou, Jun Zhang, Haijin He

Institution: Shenzhen University

Language: English

Topic: Probability and Statistical Research

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

Funders: Natural Science Foundation of Guangdong Province, Shenzhen University, Science and Technology Planning Project of Shenzhen Municipality

Award: G3920958834