Accession Number:

ADA181394

Title:

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

Descriptive Note:

Technical rept.,

Corporate Author:

WASHINGTON UNIV SEATTLE DEPT OF STATISTICS

Personal Author(s):

Report Date:

1986-08-01

Pagination or Media Count:

26.0

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.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE