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Nonparametric bayesian lifetime data analysis using dirichlet process lognormal mixture model
Author(s) -
Cheng Nan,
Yuan Tao
Publication year - 2013
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.21529
Subject(s) - dirichlet process , gibbs sampling , weibull distribution , log normal distribution , dirichlet distribution , nonparametric statistics , bayesian probability , latent dirichlet allocation , mixture model , kernel (algebra) , computer science , parametric statistics , hierarchical dirichlet process , mathematics , statistics , artificial intelligence , topic model , mathematical analysis , combinatorics , boundary value problem
We propose a nonparametric Bayesian lifetime data analysis method using the Dirichlet process mixture model with a lognormal kernel. A simulation‐based algorithm that implements the Gibbs sampling is developed to fit the Dirichlet process lognormal mixture (DPLNM) model using rightly censored failure time data. Five examples are used to illustrate the proposed method, and the DPLNM model is compared to the Dirichlet process Weibull mixture (DPWM) model. Results indicate that the DPLNM model is capable of estimating different lifetime distributions. The DPLNM model outperforms the DPWM model in all the examples, and the DPLNM model shows promising potential to be applied to analyze failure time data when an appropriate parametric model for the data cannot be specified. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013