Accurate tests for the equality of coefficients of variation. Issue 18 (12th December 2018)

• Record Type: Journal Article
• Authors: Shi, X.; Wong, A.
• Is Part Of: Journal of statistical computation and simulation. Volume 88:Issue 18(2018)
• Page Start: 3529
• Page End: 3543
• Publication Date: 2018-12-12
• Subjects: Bartlett correction -- Feltz and Miller test -- log likelihood ratio test -- Miller test -- non-parametric test -- saddlepoint approximation; 62F03 -- 62G10; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2018.1524894
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: A frequently encountered statistical problem is to determine if the variability among k populations is heterogeneous. If the populations are measured using different scales, comparing variances may not be appropriate. In this case, comparing coefficient of variation (CV) can be used because CV is unitless. In this paper, a non-parametric test is introduced to test whether the CVs from k populations are different. With the assumption that the populations are independent normally distributed, the Miller test, Feltz and Miller test, saddlepoint-based test, log likelihood ratio test and the proposed simulated Bartlett-corrected log likelihood ratio test are derived. Simulation results show the extreme accuracy of the simulated Bartlett-corrected log likelihood ratio test if the model is correctly specified. If the model is mis-specified and the sample size is small, the proposed test still gives good results. However, with a mis-specified model and large sample size, the non-parametric test is recommended.