Inference and Prediction for a General Order Statistic Model with Unknown Population Size.

Abstract

Suppose that the first n order statistics from a random sample of N positive random variables are observed, where N is unknown. A Bayes empirical Bayes approach to inference is presented. This permits the comparison of competing, perhaps non-nested, models in a natural way, and also provides easily implemented inference and prediction procedures which avoid the difficulties of non-Bayesian methods. Applications to three software reliability data sets indicate that the much-used exponential order statistic model may give rather optimistic estimates of system reliability, while the, not previously considered, Weibull order statistic model seems promising for such applications. Keywords: Pareto order statistic model; Software reliability.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1986
Accession Number
ADA181394

Entities

People

  • Adrian Raftery

Organizations

  • University of Washington

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Command And Control Systems
  • Computer Programs
  • Data Science
  • Data Sets
  • Debugging
  • Information Science
  • Military Research
  • Order Statistics
  • Probability
  • Random Variables
  • Reliability
  • Software Development
  • Statistical Analysis
  • Statistical Samples
  • Statistics
  • Systems Science
  • Universities

Fields of Study

  • Mathematics

Readers

  • Parallel and Distributed Computing.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference