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Nonparametric bayesian lifetime data analysis using dirichlet process lognormal mixture model

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  • Nan Cheng
  • Tao Yuan

Abstract

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

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  • Nan Cheng & Tao Yuan, 2013. "Nonparametric bayesian lifetime data analysis using dirichlet process lognormal mixture model," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(3), pages 208-221, April.
    • Handle: RePEc:wly:navres:v:60:y:2013:i:3:p:208-221
    DOI: 10.1002/nav.21529
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    References listed on IDEAS

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    1. Mazzuchi, Thomas A. & Singpurwalla, Nozer D., 1985. "A bayesian approach to inference for monotone failure rates," Statistics & Probability Letters, Elsevier, vol. 3(3), pages 135-141, June.
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    Cited by:

    1. Santos, Cristiano C. & Loschi, Rosangela H., 2020. "Semi-parametric Bayesian models for heterogeneous degradation data: An application to laser data," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. Nezameddin Faghih & Ebrahim Bonyadi & Lida Sarreshtehdari, 2021. "On the utility of the stochastic processes in modeling the nexus between entrepreneurship and innovation: a nonparametric application of Bayesian inference," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 11(1), pages 97-111, December.

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