Regression analysis of hierarchical Poisson-like event rate data
Subtitle: superpopulation model effect on predictions
By: Donald P. Gaver, Patricia A. Jacobs, I.G. O'Muircheartaigh
Contributors: Jacobs, Patricia A.; O'Muircheartaigh, I. G.; Naval Postgraduate School (U.S.). Dept. of Operations Research
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1990
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 19
Subjects: BAYES THEOREM
Other titles: NPS-55-90-19.
Description: This paper studies prediction of future failure (rates) by hierarchical empirical Bayes (EB)      Poisson regression methodologies. Both a gamma distributed super-population as well as a      more robust (long-tailed) log student- t super-population are considered. Simulation results      are reported concerning predicted Poisson rates. The results tentatively suggest that a      hierarchical model with gamma super-population can effectively adapt to data coming from a      log-Student-t-super-population particularly if the additional computation involved with      estimation for the log-Student-t hierarchical model is burdensome.
Notes: Cover title.

"NPS-55-90-19."

"August 1990."

AD-A230 297.

Includes bibliographical references (p. 18-19).