1,720,971 research outputs found
Nonparametric Bayesian mixture modelling for failure time data
No full textWe fit a Bayesian semiparametric accelerated failure time
mixed-effects model to a classical Kevlar fibre lifetime dataset (with censoring). The error is a shape-scale mixture of Weibull densities, mixed by a normalized generalized gamma random measure, encompassing the Dirichlet process. We implement an MCMC scheme, obtaining
posterior credibility intervals for the predictive distributions and
for the quantiles of the failure times under different stress levels. Random spool effects are taken up by the nonparametric mixture, where every component accounts for a different spool. Compared to previous analyses, we obtain narrower credibility intervals and a better fit to the data
A Bayesian framework for describing and predicting the stochastic demand of home care patients
Full text linkHome care providers are complex structures which include medical,
paramedical and social services delivered to patients at their domicile. High randomness affects the service delivery, mainly in terms of unplanned changes in patients’ conditions, which make the amount of required visits highly uncertain. Hence, each reliable and robust resource planning should include the estimation of
the future demand for visits from the assisted patients. In this paper, we propose a Bayesian framework to represent the patients’ demand evolution along with the time and to predict it in future periods. Patients’ demand evolution is described by means of a generalized linear mixed model, whose posterior densities of parameters are
obtained through Markov chain Monte Carlo simulation. Moreover, prediction of patients’ demands is given in terms of their posterior predictive probabilities. In the literature, the stochastic description of home care patients’ demand is only marginally addressed and no Bayesian approaches exist to the best of our knowledge. Results from the application to a relevant real case show the applicability of the
proposed model in the practice and validate the approach, since parameter densities in accordance to clinical evidences and low prediction errors are found
A Bayesian nonparametric model for density and cluster estimation: the epsilon-NGG process mixture.
No full textBayesian semiparametric inference for the accelerated failure time model using hierarchical mixture modeling with N-IG priors
No full text
A comparison of nonparametric priors in hierarchical mixture modelling for AFT regression
No full textWe will pursue a Bayesian nonparametric approach in the hierarchical mixture modelling of lifetime data in two situations: density estimation, when the distribution is a mixture of parametric densities with a nonparametric mixing measure, and accelerated failure time (AFT) regression modelling, when the same type of mixture is used for the distribution of the error term. The Dirichlet process is a popular choice for the mixing measure, yielding a Dirichlet process mixture model for the error; as an alternative, we also allow the
mixing measure to be equal to a normalized inverse-Gaussian prior, built from normalized inverse-Gaussian finite dimensional distributions, as recently proposed in the literature.
Markov chain Monte Carlo techniques will be used to estimate the predictive distribution of the survival time, along with the
posterior distribution of the regression parameters. A comparison between the two models will be carried out on the grounds of their predictive power and their ability to identify the number of components in a given mixture density
A semiparametric Bayesian generalized linear mixed model for the reliability of Kevlar fibres.
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