Nonparametric Interval Estimators for the Coefficient of Variation

Wang Dongliang (), Formica Margaret K. and Liu Song
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Wang Dongliang: Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
Formica Margaret K.: Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NYUSA
Liu Song: Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NYUSA

The International Journal of Biostatistics, 2018, vol. 14, issue 1, 15

Abstract: The coefficient of variation (CV) is a widely used scaleless measure of variability in many disciplines. However the inference for the CV is limited to parametric methods or standard bootstrap. In this paper we propose two nonparametric methods aiming to construct confidence intervals for the coefficient of variation. The first one is to apply the empirical likelihood after transforming the original data. The second one is a modified jackknife empirical likelihood method. We also propose bootstrap procedures for calibrating the test statistics. Results from our simulation studies suggest that the proposed methods, particularly the empirical likelihood method with bootstrap calibration, are comparable to existing methods for normal data and yield better coverage probabilities for nonnormal data. We illustrate our methods by applying them to two real-life datasets.

Keywords: coefficient of variation; empirical likelihood; Jackknife empirical likelihood; bootstrap; Wilks’ theorem (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/ijb-2017-0041

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