Confidence Intervals for Comparing Two Correlated Inverse Coefficients of Variation

Thomas M. Menino Convention & Exhibition Center 

The coefficient of variation (CV) is a widely used dimensionless measure of relative variability and plays an important role in quantifying measurement error. Its inverse, the inverse coefficient of variation (ICV), though less commonly discussed, provides a meaningful alternative, particularly when the population mean is near zero. Despite its practical relevance, statistical inference for the ICV remains underdeveloped, especially in correlated and non-normal settings.
This study addresses inference for comparing two inverse coefficients of variation when the estimates are correlated, a situation that commonly arises in paired designs, such as measurements obtained from the same subjects using different devices or outcomes. We propose nonparametric methods and an adjusted asymptotic approach for constructing confidence intervals for two correlated ICVs.
Extensive simulation studies were conducted under normal and non-normal distributional settings using Google Colab (v6e-1 TPU). In addition, biometric and lifestyle data collected by the Institute of Clinical Bioethics at Saint Joseph's University from underrepresented communities in Philadelphia were used as an application.

Inverse coefficient of variation

Confidence intervals

Skewed distributions

Nonparametric methods

Adjusted asymptotic approach

Likelihood-based methods 

Section on Nonparametric Statistics