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Statistical analysis of semi-Markov processes based on the theory of counting processes

  • Niels Keiding

Abstract

Many applications of stochastic process models in biostatistics involve several time-scales simultaneously.

Keywords

Sojourn Time Counting Process Censor Survival Data Death Intensity Multiplicative Intensity Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Aalen, O.O. (1975). Statistical inference for a family of counting processes. Ph.D. Dissertation, Dept.of Stat., Univ.of Calif., Berkeley.Google Scholar
  2. Aalen, O.O. (1978). Nonparametric inference for a family of counting processes. Ann. Statist. 6, 701–726.MathSciNetzbMATHCrossRefGoogle Scholar
  3. Aalen, O.O. (1980). A model for nonparametric regression analysis of counting processes. Springer Lect. Notes in Statist. 2, 1–25.MathSciNetCrossRefGoogle Scholar
  4. Aitkin, M., N. Laird & B. Francis (1983). A reanalysis of the Stanford heart transplant data (with discussion). J. Amer. Statist. Assoc. 78, 264–292.CrossRefGoogle Scholar
  5. Andersen, P.K., K. Borch-Johnsen, T. Deckert, A. Green, P. Hougaard, N. Keiding & S. Kreiner (1986). A Cox regression model for the relative mortality and its application to diabetes mellitus survival data. Biometrics 42 (to appear).Google Scholar
  6. Andersen, P.K. & Ø. Borgan (1985). Counting process models for life history data: A review (with discussion). Scand. J. Statist. 12, 97–158.MathSciNetzbMATHGoogle Scholar
  7. Andersen, P.J., Ø. Borgan, R.D. Gill & N. Keiding (1982). Linear non-parametric tests for comparison of countig processes, with applications to censored survival data (with discussion). Int. Statist. Rev. 50, 219–258.MathSciNetzbMATHCrossRefGoogle Scholar
  8. Andersen, P.K. & R.D. Gill (1982). Cox’s regression model for counting processes: a large sample study. Ann. Statist. 10, 1100–1120.MathSciNetzbMATHCrossRefGoogle Scholar
  9. Andersen, P.K. & N.K. Rasmussen (1986). Psychiatric admissions and choice of abortion. Statist. in Medicine 5 (to appear).Google Scholar
  10. Cox, D.R. (1986). Some remarks on semi-Markov processes in medical statistics. This volume.Google Scholar
  11. Cox, D.R. & D. Oakes (1984). Analysis of survival data. Chapman and Hall, London.Google Scholar
  12. Crowley, J. & M. Hu (1977). Covariance analysis of heart transplant survival data. J. Amer. Statist. Assoc. 73, 27–36.CrossRefGoogle Scholar
  13. Fleming, T.R, (1978). Nonparametric estimation for nonhomogeneous Markov processes in the problem of competing risks. Ann. Statist. 6, 1057–1070.MathSciNetzbMATHCrossRefGoogle Scholar
  14. Gill, R.D. (1980). Nonparametric estimation based on censored observations of a Markov renewal process. Z. Wahrscheinlichkeitsthe.verw.Geb. 53, 97–116.zbMATHCrossRefGoogle Scholar
  15. Gill, R.D. (1983). Discussion of the papers by Helland and Kurtz. Bull.Internat.Statist.Inst. 50(3), 239–243.Google Scholar
  16. Janssen, J. & R.de Dominicis (1984). Finite non-homogeneous semi-Markov processes: Theoretical and computational aspects. Insurance: Math.and Econ. (To appear).Google Scholar
  17. Lagakos, S.W., C.J. Sommer & M. Zelen (1978). Semi-Markov models for partially censored data. Biometrika 65, 311–317.MathSciNetzbMATHCrossRefGoogle Scholar
  18. Temkin, N. (1978). An analysis for transient states with application to tumor shrinkage. Biometrics 34, 571–580.zbMATHCrossRefGoogle Scholar
  19. Voelkel, J.G. & J. Crowley (1984). Nonparametric inference for a class of semi-Markov processes with censored observations. Ann.Statist. 12, 142–160.MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1986

Authors and Affiliations

  • Niels Keiding
    • 1
  1. 1.Statistical Research UnitUniversity of CopenhagenDenmark

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