The aim of the present study is to obtain and compare confidence intervals for the variance of a Gaussian distribution. Considering respectively the square error and the Higgins-Tsokos loss functions, approximate Bayesian confidence intervals for the variance of a normal population are derived. Using normal data and SAS software, the obtained approximate Bayesian confidence intervals will then be compared to the ones obtained with the well known classical method. The Bayesian approach relies only on the observations. It is shown that the proposed approximate Bayesian approach relies only on the observations. The classical method, that uses the Chi-square statistic, does not always yield the best confidence intervals.
Camara, Vincent A. R.
"Approximate Bayesian Confidence Intervals For The Variance Of A Gaussian Distribution,"
Journal of Modern Applied Statistical Methods:
2, Article 8.
Available at: http://digitalcommons.wayne.edu/jmasm/vol2/iss2/8