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Fitting a mixture distribution to complex censored survival data using generalized linear models

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  • A. J. Scallan

Abstract

Mixture models may arise for a variety of reasons in survival data analysis. This paper shows how such models that involve potentially complex cross-classification by covariates may be easily fitted using a package such as GLIM. The method employs an auxiliary Poisson-binomial model in order to find the maximum-likelihood estimates of the model parameters, and has been implemented using GLIM macros.

Suggested Citation

  • A. J. Scallan, 1999. "Fitting a mixture distribution to complex censored survival data using generalized linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(6), pages 747-753.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:747-753
    DOI: 10.1080/02664769922188
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    References listed on IDEAS

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    1. Murray Aitkin & David Clayton, 1980. "The Fitting of Exponential, Weibull and Extreme Value Distributions to Complex Censored Survival Data Using Glim," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 156-163, June.
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