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Additive Positive Stable Frailty Models

Author

Listed:
  • Madhuja Mallick

    (Merck Research Laboratories, RY34-A316)

  • Nalini Ravishanker

    (University of Connecticut)

Abstract

In this article, we describe an additive stable frailty model for multivariate times to events data using a flexible baseline hazard, and assuming that the frailty component for each individual is described by additive functions of independent positive stable random variables with possibly different stability indices. Dependence properties of this frailty model are investigated. To carry out inference, the likelihood function is derived by replacing high-dimensional integration by Monte Carlo simulation. Markov chain Monte Carlo algorithms enable estimation and model checking in the Bayesian framework.

Suggested Citation

  • Madhuja Mallick & Nalini Ravishanker, 2006. "Additive Positive Stable Frailty Models," Methodology and Computing in Applied Probability, Springer, vol. 8(4), pages 541-558, December.
    • Handle: RePEc:spr:metcap:v:8:y:2006:i:4:d:10.1007_s11009-006-0428-7
    DOI: 10.1007/s11009-006-0428-7
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    References listed on IDEAS

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    1. Dani Gamerman, 1991. "Dynamic Bayesian Models for Survival Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 63-79, March.
    2. 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.
    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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    Cited by:

    1. Mitra Rahimzadeh & Ebrahim Hajizadeh & Farzad Eskandari, 2011. "Non-mixture cure correlated frailty models in Bayesian approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1651-1663, August.

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