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All issues Volume 20 (2016) ESAIM: PS, 20 (2016) 66-94 References
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Issue
ESAIM: PS
Volume 20, 2016
Page(s) 66 - 94
DOI https://doi.org/10.1051/ps/2016004
Published online 14 July 2016
  1. G. Adimari, Empirical likelihood type confidence intervals under random censorship. Ann. Inst. Statist. Math. 49 (1997) 447–466. [CrossRef] [MathSciNet] [Google Scholar]
  2. P.K. Andersen, Ø. Borgan, R.D. Gill and N. Keiding, Statistical models based on counting processes. Springer Ser. Stat. Springer-Verlag, New York (1993). [Google Scholar]
  3. Y. Bilias, M. Gu and Zh. Ying, Towards a general asymptotic theory for Cox model with staggered entry. Ann. Statist. 25 (1997) 662–682. [CrossRef] [MathSciNet] [Google Scholar]
  4. J. Cai and D.E. Schaubel, Analysis of recurrent event data. In Advances in survival analysis, Vol. 23 of Handbook of Statist. Elsevier, Amsterdam (2004) 603–623. [Google Scholar]
  5. J. Cai and D.E. Schaubel, Marginal means/rates models for multiple type recurrent event data. Lifetime Data Anal. 10 (2004) 121–138. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  6. B.E. Chen and R.J. Cook, Tests for multivariate recurrent events in the presence of a terminal event. Biostatistics 5 (2004) 237–251. [Google Scholar]
  7. B.E. Chen, R.J. Cook, J.F. Lawless and M. Zhan, Statistical methods for multivariate interval-censored recurrent events. Stat. Med. 24 (2005) 671–691. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  8. J. Chen, L. Peng and Y. Zhao, Empirical likelihood based confidence intervals for copulas. J. Multivariate Anal. 100 (2009) 137–151 [CrossRef] [MathSciNet] [Google Scholar]
  9. S.X. Chen and I. Van Keilegom, A review on empirical likelihood methods for regression. TEST 18 (2009) 415–447. [CrossRef] [MathSciNet] [Google Scholar]
  10. S.X. Chen, L. Peng and Y. Qin, Effects of data dimension on empirical likelihood. Biometrika 96 (2009) 711–722. [CrossRef] [MathSciNet] [Google Scholar]
  11. R.J. Cook and J.F. Lawless, Marginal analysis of recurrent events and a terminating event. Stat. Med. 16 (1997) 911–924. [CrossRef] [PubMed] [Google Scholar]
  12. R.J. Cook and J.F. Lawless, The Statistical Analysis of Recurrent Events. Statistics for Biology and Health. Springer-Verlag (2007). [Google Scholar]
  13. R.J. Cook, J.F. Lawless and C. Nadeau, Robust tests for treatment comparisons based on recurrent event responses. Biometrics 52 (1996) 557–571. [CrossRef] [PubMed] [Google Scholar]
  14. R.J. Cook, J.F. Lawless, L. Lakhal-Chaieb and K. Lee, Robust estimation of mean functions and treatment effects for recurrent events under event-dependent censoring and termination: application to skeletal complications in cancer metastatic to bone. J. Amer. Statist. Assoc. 104 (2009) 60–75. [CrossRef] [MathSciNet] [Google Scholar]
  15. J-Y. Dauxois and S. Sencey, Non-parametric tests for recurrent events under competing risks. Scand. J. Stat. 36 (2009) 649–670. [CrossRef] [MathSciNet] [Google Scholar]
  16. L. Devroye and G. Lugosi, Combinatorial methods in density estimation. Springer, New York (2001). [Google Scholar]
  17. P. Du, Nonparametric modeling of the gap time in recurrent event data. Lifetime Data Anal. 15 (2009) 256–277. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  18. D. Ghosh, Accelerated rates regression models for recurrent failure time data. Lifetime Data Anal. 10 (2004) 247–261. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  19. D. Ghosh and D.Y. Lin, Nonparametric analysis of recurrent events and death. Biometrics 56 (2000) 554–562. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  20. D. Ghosh and D.Y. Lin, Marginal regression models for recurrent and terminal events. Statist. Sinica 12 (2002) 663–688. [MathSciNet] [Google Scholar]
  21. D. Ghosh and D.Y. Lin, Semiparametric analysis of recurrent events data in the presence of dependent censoring. Biometrics 59 (2003) 877–885. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  22. D.J. Hand, The statistical analysis of recurrent events by Richard R. Cook, Jerald F. Lawless. Int. Stat. Rev. 76 (2008) 142–143. [Google Scholar]
  23. N.L. Hjort, I.W. McKeague and I. Van Keilegom, Extending the scope of empirical likelihood. Ann. Statist. 37 (2009) 1079–1111. [CrossRef] [MathSciNet] [Google Scholar]
  24. B. Jing, J. Yuan and W. Zhou, Jackknife empirical likelihood. J. Amer. Statist. Assoc. 104 (2009) 1224–1232. [CrossRef] [MathSciNet] [Google Scholar]
  25. A. Keziou and S. Leoni-Aubin, On empirical likelihood for semiparametric two-sample density ratio models. J. Statist. Plann. Inference 138 (2008) 915–928. [CrossRef] [MathSciNet] [Google Scholar]
  26. J.F. Lawless, The analysis of recurrent events for multiple subjects. J. Roy. Statist. Soc. Ser. C 44 (1995) 487–498. [Google Scholar]
  27. J.F. Lawless and C. Nadeau, Some simple robust methods for the analysis of recurrent events. Technometrics 37 (1995) 158–168. [CrossRef] [MathSciNet] [Google Scholar]
  28. G. Li, R. Li and M. Zhou, Empirical likelihood in survival analysis. In Contemporary multivariate analysis and design of experiments. Vol. 2 of Ser. Biostat. World Sci. Publ., Hackensack, NJ (2005) 337–349. [Google Scholar]
  29. D.Y. Lin and Z. Ying, Nonparametric tests for the gap time distributions of serial events based on censored data. Biometrics 57 (2001) 369–375. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  30. D.Y. Lin, L.J. Wei and Zhiliang Ying. Accelerated failure time models for counting processes. Biometrika 85 (1998) 605–618. [CrossRef] [MathSciNet] [Google Scholar]
  31. D.Y. Lin, W. Sun and Z. Ying, Nonparametric estimation of the gap time distributions for serial events with censored data. Biometrika 86 (1999) 59–70. [CrossRef] [MathSciNet] [Google Scholar]
  32. D.Y. Lin, L.J. Wei, I. Yang and Z. Ying, Semiparametric regression for the mean and rate functions of recurrent events. J. R. Stat. Soc. Ser. B Stat. Methodol. 62 (2000) 711–730. [CrossRef] [MathSciNet] [Google Scholar]
  33. M. Miloslavsky, S. Keleş, M.J. van der Laan and S. Butler. Recurrent events analysis in the presence of time-dependent covariates and dependent censoring. J. R. Stat. Soc. Ser. B 66 (2004) 239–257. [CrossRef] [MathSciNet] [Google Scholar]
  34. A.B. Owen, Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75 (1988) 237–249. [CrossRef] [MathSciNet] [Google Scholar]
  35. A.B. Owen, Empirical likelihood for linear models. Ann. Statist. 19 (1991) 1725–1747. [CrossRef] [MathSciNet] [Google Scholar]
  36. A.B. Owen, Empirical likelihood ratio confidence regions. Ann. Statist. 18 (1990) 90–120. [CrossRef] [MathSciNet] [Google Scholar]
  37. A.B. Owen, Empirical Likelihood. Monographs on Statistics and Applied Probability. Taylor & Francis (2001). [Google Scholar]
  38. A. Pakes and D. Pollard, Simulation and the asymptotics of optimization estimators. Econometrica 57 (1989) 1027–1057. [CrossRef] [MathSciNet] [Google Scholar]
  39. D. Pollard, Empirical processes: theory and applications. Vol. 2 of NSF-CBMS Regional Conference Series in Probability and Statistics. Institute of Mathematical Statistics, Hayward, CA (1990). [Google Scholar]
  40. J. Ren, Weighted empirical likelihood ratio confidence intervals for the mean with censored data. Ann. Inst. Statist. Math. 53 (2001) 498–516. [CrossRef] [MathSciNet] [Google Scholar]
  41. J. Ren, Smoothed weighted empirical likelihood ratio confidence intervals for quantiles. Bernoulli 14 (2008) 725–748. [CrossRef] [MathSciNet] [Google Scholar]
  42. J. Ren, Weighted empirical likelihood in some two-sample semiparametric models with various types of censored data. Ann. Statist. 36 (2008) 147–166. [CrossRef] [MathSciNet] [Google Scholar]
  43. D.R. Thomas and G.L. Grunkemeier, Confidence interval estimation of survival probabilities for censored data. J. Amer. Statist. Assoc. 70 (1975) 865–871. [CrossRef] [MathSciNet] [Google Scholar]
  44. A.W. Van der Vaart and J.A. Wellner, Weak convergence and empirical processes. Springer (1996). [Google Scholar]
  45. D. Varron, Empirical likelihood confidence bands for functional parameters in plug-in estimation. Preprint (2014). [Google Scholar]
  46. Q. Wang and B. Jing, Empirical likelihood for a class of functionals of survival distribution with censored data. Ann. Inst. Statist. Math. 53 (2001) 517–527. [CrossRef] [MathSciNet] [Google Scholar]
  47. W. Wang and M.T. Wells, Nonparametric estimation of successive duration times under dependent censoring. Biometrika 85 (1998) 561–572. [CrossRef] [MathSciNet] [Google Scholar]

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