[1] S. Chib and E. Greenberg, Understanding the Metropolis-Hastings
algorithm, The American Statistician 49 (1995), 327-335.
[2] D. R. Cox, The statistical analysis of dependencies in point
process, Stochastic Point Processes, pp. 55-66, 1972.
[3] M. H. Gail, T. J. Santner and C. C. Brown, An analysis of
comparative carcinogenesis experiments based on multiple times to
tumor, Biometrics 36 (1980), 255-266.
[4] J. Geweke, Evaluating the accuracy of sampling-based approaches to
the calculation of posterior moments, Bayesian Statistics 4 (1992),
169-193.
[5] W. K. Hastings, Monte Carlo sampling methods using Markov chains
and their application, Biometrika 57 (1970), 247-264.
[6] J. G. Ibrahim, M. H. Chen and D. Sinha, Bayesian Survival
Analysis, Springer, New York, (2001).
[7] J. F. Lawless, Statistical Models and Methods for Lifetime Data,
Wiley, New York, (1982).
[8] J. F. Lawless and C. Nadeau, Some simple robust methods for the
analysis of recurrent events, Technometrics 37 (1995), 158-168.
[9] F. Louzada Neto, A hybrid scale intensity model for recurrent
event data, Communication in Statistics 33 (2004), 119-133.
[10] W. Nelson, Graphical analysis of the system repair data, Journal
of Quality Technology 20 (1988), 24-35.
[11] W. Nelson, Confidence limits for recurrent data-applied to cost
or number of product repairs, Technometrics 37 (1995), 147-157.
[12] M. S. Pepe and J. Cai, Some graphical displays marginal
regression analysis for recurrent failure times and time dependent
covariates, J. Amer. Stat. Assoc. 88 (1993), 811-820.
[13] R. L. Prentice, B. J. Willians and A. V. Peterson, On the
regression analysis of multivariate failure time data, Biometrika 68
(1981), 373-379.
[14] R Development Core Team, R, A Language and Environment for
Statistical Computing, R Foundation for Statistical Computing, Vienna,
Austria, (2006).
[15] L. J. Wei, D. Y. Lin and L. Weissefeld, Regression analysis of
multivariate incomplete failure time data by modelling marginal
distributions, J. Amer. Stat. Assoc. 84 (1989), 100-116.