Regression Analysis of Hierarchical Poisson-like Event Rate Data: Superpopulation Model Effect on Predictions
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Authors
Gaver, Donald Paul
O'Muircheartaigh, I. G.
Jacobs, Patricia A.
Subjects
Empirical Bayes prediction
hierarchical models
extra-Poisson variability
Poisson regression
gamma superpopulation
log student-t superpopulation
hierarchical models
extra-Poisson variability
Poisson regression
gamma superpopulation
log student-t superpopulation
Advisors
Date of Issue
1990-08
Date
1990-08
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
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
Type
Technical Report
Description
Series/Report No
Department
Identifiers
NPS Report Number
NPS-55-90-19
Sponsors
Naval Postgraduate School Research Council Research Program.
Funder
O&MN, Direct Funding
Format
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.