Regression Analysis of Hierarchical Poisson-Like Event Rate Data: Super- Population Model Effect on Predictions

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.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1990
Accession Number
ADA230297

Entities

People

  • Donald P. Gaver Jr.
  • I. G. O'muircheartaigh
  • Patricia A. Jacobs

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Business Administration
  • Computations
  • Equations
  • Estimators
  • Health
  • Industrial Engineering
  • Mathematics
  • Military Research
  • Operations Research
  • Public Health
  • Random Variables
  • Regression Analysis
  • Simulations
  • Statistics
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Distributed Systems and Data Platform Development
  • Statistical inference.
  • Systems Analysis and Design