Adjusted generalized confidence intervals for the common coefficient of variation of several normal populations

摘要

This paper proposes the novel approaches to construct confidence intervals for the common coefficient of variation (CV) of several normal populations using the concepts of the adjusted generalized confidence interval (adjusted GCI) approach and the computational approach. The coverage probability and average length of the proposed approaches were evaluated by a Monte Carlo simulation and compared with that of the existing approaches; the generalized confidence interval (GCI) approach and the adjusted method of variance estimates recovery (adjusted MOVER) approach. The results showed that the coverage probability of the adjusted GCI approach is above the nominal confidence level of 0.95 and better than the other approaches when the sample case is small (k <= 6) for all sample sizes, except when the sample sizes are very small. However, the coverage probability of the computational approach provides much better confidence interval estimate than the other approaches when the sample case is large (k >= 10). The proposed approaches and the existing approaches are illustrated using three medical science data.