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Semiparametric Analysis of Degradation and Failure Time Data with Covariates

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Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life

Part of the book series: Statistics for Industry and Technology ((SIT))

Abstract

The paper considers the semiparametric analysis of several new degradation and failure time regression models without and with time depending covariates. These joint models for survival and longitudinal data measured with errors can be applied in studies of longevity, aging and degradation in survival analysis, biostatistics, epidemiology, demography, oncology, biology and reliability.

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References

  1. Andersen, P. K., Borgan, O., Gill, R. and Keiding, N. (1993). Statistical Models Based on the Counting Processes, Springer-Verlag, New York.

    Book  Google Scholar 

  2. Bagdonavičius, V. and Nikulin, M. (2001). Estimation in degradation models with explanatory variables, Lifetime Data Analysis, 7, 85–103.

    Article  MATH  MathSciNet  Google Scholar 

  3. Bagdonavičius, V. and Nikulin, M. (2002). Accelerated Life Models: Modeling and Statistical Analysis. Chapman & Hall/CRC, Boca Raton, Florida.

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  4. Bagdonavičius, V., Bikelis, A., Kazakevicius, A. and Nikulin, M. (2002). Non-parametric estimation from simultaneous degradation and failure data, Comptes Rendus, Académie des Sciences de Paris, 335, 183–188.

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  5. Bagdonavičius, V., Bikelis, A., Kazakevičius, A. and Nikulin, M. (2003). Estimation from simultaneous degradation and failure time data, In Mathematical and Statistical Methods in Reliability (Eds., B. Lindqvist and K. A. Doksum), pp. 301–318, World Scientific Publishing, Singapore.

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  6. Zdorova-Cheminade, O. (2003). Modélisation du processus d’évolution de l’incapacité chez les personnes agées, Mémoire de DEA “Epidémiologie et Intervention en Santé Publique”, Université Bordeaux 2, Juin 2003, 77 pages.

    Google Scholar 

  7. Meeker, W. Q. and Escobar, L. (1998). Statistical Analysis for Reliability Data, John Wiley & Sons, New York.

    Google Scholar 

  8. Wulfsohn, M. and Tsiatis, A. (1997). A joint model for survival and longitudinal data measured with error, Biometrics, 53, 330–339.

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematical Statistics, University of Vilnius, Vilnius, Lithuania

    V. Bagdonavičius

  2. Victor Segalen University Bordeaux 2, Bordeaux, France & Laboratory of Statistical Methods, Steklov Mathematical Institute, Saint Petersburg, Russia

    M. Nikulin

Authors
  1. V. Bagdonavičius
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  2. M. Nikulin
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, L8S 4K1, Canada

    N. Balakrishnan

  2. Laboratoire Statistique Mathématique, Université Bordeaux 2, 33076, Bordeaux Cedex, France

    M. S. Nikulin

  3. Laboratory of Statistical Methods, V. Steklov Mathematical Institute, 191011, St. Petersburg, Russia

    M. S. Nikulin

  4. UFR SSI, Université de Bretagne-Sud, 556017, Vannes Cedex, France

    M. Mesbah

  5. Division Mathématiques Appliquées, Université de Technologie de Compiègne, Compiègne Cedex, 60205, France

    N. Limnios

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© 2004 Springer Science+Business Media New York

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Bagdonavičius, V., Nikulin, M. (2004). Semiparametric Analysis of Degradation and Failure Time Data with Covariates. In: Balakrishnan, N., Nikulin, M.S., Mesbah, M., Limnios, N. (eds) Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life. Statistics for Industry and Technology. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-8206-4_4

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  • DOI: https://doi.org/10.1007/978-0-8176-8206-4_4

  • Publisher Name: Birkhäuser, Boston, MA

  • Print ISBN: 978-1-4612-6491-0

  • Online ISBN: 978-0-8176-8206-4

  • eBook Packages: Springer Book Archive

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