
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 nonnested, models in a natural way, and also provides easily implemented inference and prediction procedures which avoid the difficulties of nonBayesian methods. Applications to three software reliability data sets indicate that the muchused 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