Accession Number : ADA083175


Title :   Bayesian Nonparametric Statistical Inference for Shock Models and Wear Processes.


Descriptive Note : Research rept.,


Corporate Author : CALIFORNIA UNIV BERKELEY OPERATIONS RESEARCH CENTER


Personal Author(s) : Lo,Albert Y


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a083175.pdf


Report Date : Dec 1979


Pagination or Media Count : 24


Abstract : Statistical procedures for shock models and wear processes are considered in this paper. We show that independent gamma-Dirichlet priors are conjugate priors when sampling from these shock models. Bayes rules given the observations are computed. In particular, we calculate the Bayes estimates of the survival probabilities for these models. We show consistency of the posterior distribution as well as weak convergence of the centered and suitably rescaled posterior processes. (Author)


Descriptors :   *NONPARAMETRIC STATISTICS , MATHEMATICAL MODELS , DAMAGE ASSESSMENT , CONSISTENCY , SHOCK , WEAR , STATISTICAL DISTRIBUTIONS , BAYES THEOREM , WEAK CONVERGENCE


Subject Categories : Statistics and Probability


Distribution Statement : APPROVED FOR PUBLIC RELEASE