Confidence intervals for the weighted coefficients of variation of two-parameter exponential distributions

This paper proposes new confidence intervals for the weighted coefficients of variation (CV) of two-parameter exponential distributions based on the adjusted method of variance estimates recovery method (adjusted MOVER). This is then compared with the generalized confidence interval method (GCI) and the large sample method. The performance of these confidence intervals in terms of coverage probabilities and average lengths were evaluated via a Monte Carlo simulation. Simulation studies showed that the GCI should be considered as an alternative to the confidence interval estimation for the weighted CV of two-parameter exponential distributions. However, the adjusted MOVER confidence interval (CIAM2) can be used to estimate the weighted CV when the coefficient of variation is a positive value. The proposed confidence intervals are illustrated using a real example.