References

 

  1. Andersen P.K, Ø Borgan, R.D. Gill and N. Keiding (1993). Statistical Models Based on Counting Processes. Springer-Verlag.
  2. Müller P. and R. Mitra (2013). Bayesian nonparametric inference – why and how. Bayesian anal. 8(2), 269-302.
  3. Arjas E. and D. Gasbarra (1994). Nonparametric Bayesian inference from right-censored survival data, using Gibbs sampler. Statistica Sinica 4, 505-524. PDF.
  4. Härkänen T. (2003). BITE: a Bayesian Intensity Estimator. Computational Statistics 18, 565-583.
  5. Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711-732.
  6. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. Bayesian data analysis. 2013. Chapman Hall, London.
  7. Robert, C., & Casella, G. (2013). Monte Carlo statistical methods. Springer Science & Business Media.
  8. Arjas, E., Keiding, N., Borgan, Ø., Andersen, P. K., & Natvig, B. (1989). Survival Models and Martingale Dynamics [with Discussion and Reply]. Scandinavian Journal of Statistics, 177-225. PDF.
  9. Keiding, N. (2014). Event history analysis. Annual Review of Statistics and its Application, 1, 333-360. PDF.
  10. Walker, S. G., Damien, P., Laud, P. W., & Smith, A. F. (1999). Bayesian nonparametric inference for random distributions and related functions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3), 485-527. PDF.

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