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) : Raftery,Adrian E

PDF Url : ADA181394

Report Date : Aug 1986

Pagination or Media Count : 26

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.

Descriptors :   *ORDER STATISTICS, *STATISTICAL INFERENCE, BAYES THEOREM, SIZES(DIMENSIONS), COMPUTER PROGRAM RELIABILITY, ESTIMATES, RELIABILITY, RANDOM VARIABLES, NUMERICAL METHODS AND PROCEDURES, PREDICTIONS, MATHEMATICAL MODELS, MATHEMATICAL PREDICTION, POPULATION(MATHEMATICS)

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE