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Bivariate positive stable frailty models

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  • Mallick, Madhuja
  • Ravishanker, Nalini
  • Kannan, Nandini

Abstract

This article describes inference for dependent multivariate times-to-events using a bivariate positive stable frailty model with a Weibull baseline hazard. Suitable Markov chain Monte Carlo algorithms facilitate Bayesian inference. The method is illustrated using a study conducted by the Air Force Research Laboratory on times to symptoms of decompression sickness in human subjects.

Suggested Citation

  • Mallick, Madhuja & Ravishanker, Nalini & Kannan, Nandini, 2008. "Bivariate positive stable frailty models," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2371-2377, October.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:15:p:2371-2377
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    References listed on IDEAS

    as
    1. Zuqiang Qiou & Nalini Ravishanker & Dipak K. Dey, 1999. "Multivariate Survival Analysis with Positive Stable Frailties," Biometrics, The International Biometric Society, vol. 55(2), pages 637-644, June.
    2. Abdul-Hamid, Husein & Nolan, John P., 1998. "Multivariate Stable Densities as Functions of One Dimensional Projections," Journal of Multivariate Analysis, Elsevier, vol. 67(1), pages 80-89, October.
    3. Chris Elbers & Geert Ridder, 1982. "True and Spurious Duration Dependence: The Identifiability of the Proportional Hazard Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(3), pages 403-409.
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