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Weibull Extension of Bivariate Exponential Regression Model with Gamma Frailty for Survival Data

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  • Hanagal David D.

    (Department of Statistics, University of Pune, Pune-411007, India)

Abstract

A maximum likelihood estimation procedure is developed for a bivariate frailty regression model, in which dependence is generated by a gamma distribution. It is assumed that the random lifetimes follow a bivariate Weibull distribution proposed in Hanagal [Economic Quality Control 11: 193-200, 1996] and that censoring is independent of the two lifetimes. The proposed model may be applied for survival times in genetic epidemiology, survival times of dental implants of patients and survival times of twin births (both monozygotic and dizygotic), with genetic frailty behavior (which is unknown and random) of patients.

Suggested Citation

  • Hanagal David D., 2006. "Weibull Extension of Bivariate Exponential Regression Model with Gamma Frailty for Survival Data," Stochastics and Quality Control, De Gruyter, vol. 21(2), pages 261-270, January.
    • Handle: RePEc:bpj:ecqcon:v:21:y:2006:i:2:p:261-270:n:9
    DOI: 10.1515/EQC.2006.261
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    References listed on IDEAS

    as
    1. Lee, Larry, 1979. "Multivariate distributions having Weibull properties," Journal of Multivariate Analysis, Elsevier, vol. 9(2), pages 267-277, June.
    2. David Hanagal, 2006. "Bivariate Weibull regression model based on censored samples," Statistical Papers, Springer, vol. 47(1), pages 137-147, January.
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    Cited by:

    1. David Hanagal, 2009. "Weibull extension of bivariate exponential regression model with different frailty distributions," Statistical Papers, Springer, vol. 50(1), pages 29-49, January.
    2. Hanagal, David D., 2008. "Modelling heterogeneity for bivariate survival data by the log-normal distribution," Statistics & Probability Letters, Elsevier, vol. 78(9), pages 1101-1109, July.
    3. Hanagal, David D., 2010. "Modeling heterogeneity for bivariate survival data by the compound Poisson distribution with random scale," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1781-1790, December.

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