Predictive analyses for nonhomogeneous Poisson processes with power law using Bayesian approach
| Autor: | Man-Lai Tang, Guo-Liang Tian, Jun-Wu Yu |
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| Rok vydání: | 2007 |
| Předmět: |
Statistics and Probability
Applied Mathematics Bayesian probability Prediction interval Poisson distribution Industrial engineering Software quality Computational Mathematics symbols.namesake Computational Theory and Mathematics Prior probability symbols Statistical inference Econometrics Mathematics Statistical hypothesis testing Weibull distribution |
| Zdroj: | Computational Statistics & Data Analysis. 51:4254-4268 |
| ISSN: | 0167-9473 |
| DOI: | 10.1016/j.csda.2006.05.010 |
| Popis: | Nonhomogeneous Poisson process (NHPP) also known as Weibull process with power law, has been widely used in modeling hardware reliability growth and detecting software failures. Although statistical inferences on the Weibull process have been studied extensively by various authors, relevant discussions on predictive analysis are scattered in the literature. It is well known that the predictive analysis is very useful for determining when to terminate the development testing process. This paper presents some results about predictive analyses for Weibull processes. Motivated by the demand on developing complex high-cost and high-reliability systems (e.g., weapon systems, aircraft generators, jet engines), we address several issues in single-sample and two-sample prediction associated closely with development testing program. Bayesian approaches based on noninformative prior are adopted to develop explicit solutions to these problems. We will apply our methodologies to two real examples from a radar system development and an electronics system development. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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