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Survival Analysis Using Semiparametric Bayesian Methods

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Book cover Practical Nonparametric and Semiparametric Bayesian Statistics

Part of the book series: Lecture Notes in Statistics ((LNS,volume 133))

Abstract

This article presents the scope of nonparametric and semi-parametric Bayesian methods for the analysis of survival data using models based on either the hazard or the intensity function. The nonparametric part of every model is assumed to have a suitable prior process. The parametric part, which may include a regression parameter or a parameter quantifying the heterogeneity of a population, is assumed to have a prior distribution with possibly unknown hyperparameters. Careful applications of some recently popular computational tools, including MCMC algorithms, are available to perform sophisticated Bayesian analyses even when we are dealing with complex models and unusual data structures.

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Authors
  1. Debajyoti Sinha
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  2. Dipak K. Dey
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Editor information

Editors and Affiliations

  1. Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA

    Dipak Dey

  2. ISDS, Duke University, Durham, NC, 27708, USA

    Peter Müller

  3. Department of Mathematics, University of New Hampshire, Durham, NH, 03824, USA

    Debajyoti Sinha

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© 1998 Springer Science+Business Media New York

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Sinha, D., Dey, D.K. (1998). Survival Analysis Using Semiparametric Bayesian Methods. In: Dey, D., Müller, P., Sinha, D. (eds) Practical Nonparametric and Semiparametric Bayesian Statistics. Lecture Notes in Statistics, vol 133. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1732-9_10

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  • DOI: https://doi.org/10.1007/978-1-4612-1732-9_10

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98517-6

  • Online ISBN: 978-1-4612-1732-9

  • eBook Packages: Springer Book Archive

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