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Using weibull mixture distributions to model heterogeneous survival data

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  • Marín Díazaraque, Juan Miguel
  • Rodríguez Bernal, María Teresa
  • Wiper, Michael Peter

Abstract

In this article we use Bayesian methods to fit a Weibull mixture model with an unknown number of components to possibly right censored survival data. This is done using the recently developed, birth-death MCMC algorithm. We also show how to estimate the survivor function and the expected hazard rate from the MCMA output.

Suggested Citation

  • Marín Díazaraque, Juan Miguel & Rodríguez Bernal, María Teresa & Wiper, Michael Peter, 2003. "Using weibull mixture distributions to model heterogeneous survival data," DES - Working Papers. Statistics and Econometrics. WS ws033208, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws033208
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    Cited by:

    1. Martin X. Dunbar & Hani M. Samawi & Robert Vogel & Lili Yu, 2014. "Steady-state Gibbs sampler estimation for lung cancer data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 977-988, May.
    2. Karl Mosler & Christoph Scheicher, 2008. "Homogeneity testing in a Weibull mixture model," Statistical Papers, Springer, vol. 49(2), pages 315-332, April.

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