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Posterior analysis of latent competing risk models by parallel tempering

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  • Kozumi, Hideo

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  • Kozumi, Hideo, 2004. "Posterior analysis of latent competing risk models by parallel tempering," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 441-458, June.
  • Handle: RePEc:eee:csdana:v:46:y:2004:i:3:p:441-458
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    1. Mazucheli, Josmar & Louzada-Neto, Francisco & Achcar, Jorge A., 2001. "Bayesian inference for polyhazard models in the presence of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 1-14, November.
    2. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    3. Song, Xin-Yuan & Lee, Sik-Yum, 2002. "A Bayesian model selection method with applications," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 539-557, September.
    4. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    5. B. Reiser & I. Guttman & Dennis K. J. Lin & Frank M. Guess & John S. Usher, 1995. "Bayesian Inference for Masked System Lifetime Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(1), pages 79-90, March.
    6. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    7. Stephen W. Lagakos & Thomas A. Louis, 1988. "Use of Tumour Lethality to Interpret Tumorigenicity Experiments Lacking Cause‐Of‐Death Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(2), pages 169-179, June.
    8. Kuo, Lynn & Yang, Tae Young, 2000. "Bayesian reliability modeling for masked system lifetime data," Statistics & Probability Letters, Elsevier, vol. 47(3), pages 229-241, April.
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