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A New Approach for the Analysis of Multi-Episode Duration Data

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Advances in GLIM and Statistical Modelling

Part of the book series: Lecture Notes in Statistics ((LNS,volume 78))

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Abstract

This contribution is concerned with the statistical analysis of multi-episode models. Semiparametric estimation procedures based on transformation models are developed for the continuous time case under the hypothesis of independent sojourn-times so that the results of Cuzick (1988), Tsiatis (1990) and Ritov (1990) are generalized to the multivariate situation of multi-episode duration data. On the other hand we relax the assumption of independent episodes and develope a semiparametric estimation procedure for conditional independent sojourn-times. In this case the frailty-concept plays the important part of generating multivariate survivor functions and the estimation is carried out using a likelihood based upon the ranks of the sojourn-times generalized for censored data.

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Author information

Authors and Affiliations

  1. Faculty of Economics and Statistics, University of Konstanz, P.O. Box 5560, FRG-7750, Konstanz, Germany

    Leo Brecht

Authors
  1. Leo Brecht
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Editor information

Editors and Affiliations

  1. Seminar für Statistik, University of Munich, D-8000, Munich 22, Germany

    Ludwig Fahrmeir

  2. Centre for Applied Statistics, Lancaster University, Fylde College, Lancaster, LA1 4YF, UK

    Brian Francis

  3. Faculty of Science, Computing and Engineering, Polytechnic of North London, London, N7 8DB, England

    Robert Gilchrist

  4. Institut für Statistik, University of Regensburg, Germany

    Gerhard Tutz

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© 1992 Springer-Verlag New York, Inc.

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Brecht, L. (1992). A New Approach for the Analysis of Multi-Episode Duration Data. In: Fahrmeir, L., Francis, B., Gilchrist, R., Tutz, G. (eds) Advances in GLIM and Statistical Modelling. Lecture Notes in Statistics, vol 78. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2952-0_6

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  • DOI: https://doi.org/10.1007/978-1-4612-2952-0_6

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97873-4

  • Online ISBN: 978-1-4612-2952-0

  • eBook Packages: Springer Book Archive

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