Abstract/Details

NON PARAMETRIC QUASI-BAYESIAN ESTIMATION OF RELIABILITY AND PRIOR DISTRIBUTION

WENG, CHENG-MING. 
 University of South Florida ProQuest Dissertations Publishing,  1980. 8022936.

Abstract (summary)

The purpose of this study is to examine from a nonparameter point of view and in a Bayesian setting failure models which depend on stochastic parameters whose distribution G((theta)) is unknown. The problem of estimating G when a priori information about G is specified in the form of an initial guess G(,0), is considered. First, assuming that the unconditional failure time distribution is a Dirichlet process, estimators of the prior G and reliability function are obtained based on censored data. Also, assuming that the unconditional failure time distribution is a mixture of Dirichlet process, Bayesian estimators are obtained for reliability and the prior distribution. Monte Carlo simulation is employed to compare the estimators for some specific failure models.

The results are extended to multiparameter models, under the assumption that the unconditional failure time distribution F(,G) is a Dirichlet process or a mixture of Dirichlet processes. In both cases, some examples are given to illustrate the usefulness of the theoretic results. Some possible extensions are mentioned.

Indexing (details)


Subject
Mathematics
Classification
0405: Mathematics
Identifier / keyword
Pure sciences
Title
NON PARAMETRIC QUASI-BAYESIAN ESTIMATION OF RELIABILITY AND PRIOR DISTRIBUTION
Author
WENG, CHENG-MING
Number of pages
83
Degree date
1980
School code
0206
Source
DAI-B 41/04, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9798660911484
University/institution
University of South Florida
University location
United States -- Florida
Degree
Ph.D.
Source type
Dissertation or Thesis
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
8022936
ProQuest document ID
303089696
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
https://www.proquest.com/docview/303089696