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Approximate Bayesian Confidence Intervals for the Variance of a Gaussian Distribution
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2002 |
| Abstract | 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. 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. In fact, the proposed approach performs often better. . |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://digitalcommons.wayne.edu/cgi/viewcontent.cgi?article=1701&context=jmasm |
| Alternate Webpage(s) | http://www.amstat.org/sections/srms/proceedings/y2002/Files/JSM2002-000092.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation algorithm CHI Confidence Intervals Higgins Loss function SAS Sample Variance Whole Earth 'Lectronic Link |
| Content Type | Text |
| Resource Type | Article |
