A semiparametric Bayesian generalized linear mixed model for the reliability of Kevlar fibres

Raffaele Argiento, Alessandra Guglielmi, Jacopo Soriano

Risultato della ricerca: Contributo in rivistaArticolo in rivista

3 Citazioni (Scopus)


We analyze the reliability of NASA composite pressure vessels by using a new Bayesian semiparametric model. The data set consists of lifetimes of pressure vessels, wrapped with a Kevlar fiber, grouped by spool, subject to different stress levels; 10% of the data are right censored. The model that we consider is a regression on the log-scale for the lifetimes, with fixed (stress) and random (spool) effects. The prior of the spool parameters is nonparametric, namely they are a sample from a normalized generalized gamma process, which encompasses the well-known Dirichlet process. The nonparametric prior is assumed to robustify inferences to misspecification of the parametric prior. Here, this choice of likelihood and prior yields a new Bayesian model in reliability analysis. Via a Bayesian hierarchical approach, it is easy to analyze the reliability of the Kevlar fiber by predicting quantiles of the failure time when a new spool is selected at random from the population of spools. Moreover, for comparative purposes, we review the most interesting frequentist and Bayesian models analyzing this data set. Our credibility intervals of the quantiles of interest for a new random spool are narrower than those derived by previous Bayesian parametric literature, although the predictive goodness- of-fit performances are similar. Finally, as an original feature of our model, by means of the discreteness of the random-effects distribution, we are able to cluster the spools into three different groups.
Lingua originaleEnglish
pagine (da-a)410-423
Numero di pagine14
RivistaApplied Stochastic Models in Business and Industry
Stato di pubblicazionePubblicato - 2012


  • Bayesian clustering
  • Bayesian nonparametrics
  • accelerated failure time regression model
  • random-effects model
  • reliability


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