Robust Empirical Bayes Analyses of Event Rates.

Abstract

A number, I, of nominally similar items generate events (e.g. failures) at possibly different rates, or mean time intervals. This paper addresses the problem of appropriately pooling the data from the different sources. The approach is parametric empirical Bayes: true individual item rates are assumed to come from a fixed superpopulation. It is shown how parameters of a superpopulation model can be estimated from all of the data, and combined with individual unit history, can provide improved estimates of individual rates. The procedure can be robust: evidence that a particular rate is far off from the main body of rates permits that outlier to stand by itself, i.e. to resist pooling. Illustrative analyses of data are supplied. Keywords: Robustness; Population(Mathematics); and Charts.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1986
Accession Number
ADA166681

Entities

People

  • Donald P. Gaver Jr.
  • Iognaid G. O'muircheartaigh

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Classification
  • Computational Science
  • Confidence Limits
  • Data Analysis
  • Data Sets
  • Estimators
  • Information Science
  • Intervals
  • Mathematics
  • Maximum Likelihood Estimation
  • Military Research
  • Operations Research
  • Public Health
  • Risk Analysis
  • Statistical Analysis
  • Statistics
  • Time Intervals

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Theoretical Analysis.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.