2015 Symposium Archive (Statesboro)
An Exact Expression for the Behrens-Fisher Distribution with Applications
Location
Room 1909
Session Format
Paper Presentation
Research Area Topic:
Natural & Physical Sciences - Mathematics
Co-Presenters and Faculty Mentors or Advisors
Fengjiao Hu
Abstract
An exact solution is given for the Behrens-Fisher distribution under the independent normal model. The cumulative distribution function (cdf) and the probability density function (pdf) are expressed as infinite series of non-central t-distributions cdfs and pdfs, respectively. It is then observed that if the means are equal the distribution depends only on the ratio of the two population variances and the sample sizes. When the means are not equal, the distribution depends on the difference of the two means, the two population variances, and the two sample sizes. Methods are given for using the exact distribution to obtain an estimated confidence interval and an estimated p-value.
Keywords
Comparing two population means, Confidence interval, p-value, Welsh’s approximation
Start Date
4-24-2015 1:30 PM
End Date
4-24-2015 2:30 PM
Copyright
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Recommended Citation
Champ, Charles W., "An Exact Expression for the Behrens-Fisher Distribution with Applications" (2015). GS4 Student Scholars Symposium. 71.
https://digitalcommons.georgiasouthern.edu/research_symposium/2015/2015/71
An Exact Expression for the Behrens-Fisher Distribution with Applications
Room 1909
An exact solution is given for the Behrens-Fisher distribution under the independent normal model. The cumulative distribution function (cdf) and the probability density function (pdf) are expressed as infinite series of non-central t-distributions cdfs and pdfs, respectively. It is then observed that if the means are equal the distribution depends only on the ratio of the two population variances and the sample sizes. When the means are not equal, the distribution depends on the difference of the two means, the two population variances, and the two sample sizes. Methods are given for using the exact distribution to obtain an estimated confidence interval and an estimated p-value.
