Confidence intervals are constructed for the coefficient of variation of a Gaussian distribution. Considering the square error and the Higgins-Tsokos loss functions, approximate Bayesian models are derived and compared to a published classical model. The models are shown to have great coverage accuracy. The classical model does not always yield the best confidence intervals; the proposed models often perform better.
Camara, Vincent A. R.
"New Approximate Bayesian Confidence Intervals for the Coefficient of Variation of a Gaussian Distribution,"
Journal of Modern Applied Statistical Methods:
1, Article 13.
Available at: http://digitalcommons.wayne.edu/jmasm/vol11/iss1/13