Free Access
Issue
ESAIM: PS
Volume 13, January 2009
Page(s) 87 - 114
DOI https://doi.org/10.1051/ps:2008004
Published online 26 March 2009
  1. M. Aitkin and D. Clayton, The fitting of exponential, Weibull and extreme value distributions to complex censored survival data using GLIM. J. R. Stat. Soc., Ser. C 29 (1980) 156–163. [Google Scholar]
  2. P.K. Andersen, O. Borgan, R.D. Gill and N. Keiding, Statistical models based on counting processes. Springer Series in Statistics (1993). [Google Scholar]
  3. T. Augustin, An exact corrected log-likelihood function for Cox's proportional hazards model under measurement error and some extensions. Scand. J. Stat. 31 (2004) 43–50. [CrossRef] [Google Scholar]
  4. Ø. Borgan, Correction to: Maximum likelihood estimation in parametric counting process models, with applications to censored failure time data. Scand. J. Statist. 11 (1984) 275. [MathSciNet] [Google Scholar]
  5. Ø. Borgan, Maximum likelihood estimation in parametric counting process models, with applications to censored failure time data. Scand. J. Stat., Theory Appl. 11 (1984) 1–16. [Google Scholar]
  6. C. Butucea and M.-L. Taupin, New M-estimators in semiparametric regression with errors in variables. Ann. Inst. Henri Poincaré: Probab. Stat. (to appear). [Google Scholar]
  7. J.S. Buzas, Unbiased scores in proportional hazards regression with covariate measurement error. J. Statist. Plann. Inference, 67 (1998) 247–257. [Google Scholar]
  8. R.J. Carroll, D. Ruppert, and L.A. Stefanski, Measurement error in nonlinear models. Chapman and Hall (1995). [Google Scholar]
  9. F. Comte and M.-L. Taupin, Nonparametric estimation of the regression function in an errors-in-variables model. Statistica Sinica 17 (2007) 1065–1090. [MathSciNet] [Google Scholar]
  10. D.R. Cox and D. Oakes, Analysis of survival data. Monographs on Statistics and Applied Probability. Chapman and Hall (1984). [Google Scholar]
  11. J. Fan and Y.K. Truong, Nonparametric regression with errors in variables. Ann. Statist. 21 (1993) 1900–1925. [CrossRef] [MathSciNet] [Google Scholar]
  12. M.V. Fedoryuk, Asimptotika: integraly i ryady. Asymptotics: Integrals and Series (1987). [Google Scholar]
  13. W.A. Fuller, Measurement error models. Wiley Series in Probability and Mathematical Statistics (1987). [Google Scholar]
  14. R.D. Gill and P.K. Andersen, Cox's regression model for counting processes: a large sample study. Ann. Statist. 10 (1982) 1100–1120. [CrossRef] [MathSciNet] [Google Scholar]
  15. G. Gong, A.S. Whittemore and S. Grosser, Censored survival data with misclassified covariates: A case study of breast-cancer mortality. J. Amer. Statist. Assoc. 85 (1990) 20–28. [CrossRef] [Google Scholar]
  16. N.L. Hjort, On inference in parametric survival data models. Int. Stat. Rev. 60 (1992) 355–387. [CrossRef] [Google Scholar]
  17. D.W.J. Hosmer and S. Lemeshow, Applied survival analysis. Regression modeling of time to event data. Wiley Series in Probability and Mathematical Statistics (1999). [Google Scholar]
  18. C. Hu and D.Y. Lin, Semiparametric failure time regression with replicates of mismeasured covariates. J. Am. Stat. Assoc. 99 (2004) 105–118. [CrossRef] [Google Scholar]
  19. C. Hu and D.Y. Lin, Cox regression with covariate measurement error. Scand. J. Stat. 29 (2002) 637–655. [CrossRef] [Google Scholar]
  20. Y. Huang and C.Y. Wang, Cox regression with accurate covariates unascertainable: A nonparametric-correction approach. J. Am. Stat. Assoc. 95 (2000) 1209–1219. [CrossRef] [Google Scholar]
  21. J. Kiefer and J. Wolfowitz, Consistency of the maximum likelihood estimator in the presence of infinitely many nuisance parameters. Ann. Math. Statist. 27 (1956) 887–906. [CrossRef] [MathSciNet] [Google Scholar]
  22. F.H. Kong, Adjusting regression attenuation in the Cox proportional hazards model. J. Statist. Plann. Inference 79 (1999) 31–44. [CrossRef] [MathSciNet] [Google Scholar]
  23. F.H. Kong and M. Gu, Consistent estimation in Cox proportional hazards model with covariate measurement errors. Statistica Sinica 9 (1999) 953–969. [MathSciNet] [Google Scholar]
  24. O.V. Lepski and B.Y. Levit, Adaptive minimax estimation of infinitely differentiable functions. Math. Methods Statist. 7 (1998) 123–156. [MathSciNet] [Google Scholar]
  25. Y. Li and L. Ryan, Survival analysis with heterogeneous covariate measurement error. J. Amer. Statist. Assoc. 99 (2004) 724-735. [CrossRef] [MathSciNet] [Google Scholar]
  26. Y. Li and L. Ryan, Inference on survival data with covariate measurement error – An imputation-based approach. Scand. J. Stat. 33 (2006) 169–190. [CrossRef] [Google Scholar]
  27. M.-L. Martin-Magniette, Nonparametric estimation of the hazard function by using a model selection method: estimation of cancer deaths in Hiroshima atomic bomb survivors. J. Roy. Statist. Soc. Ser. C 54 (2005) 317–331. [CrossRef] [MathSciNet] [Google Scholar]
  28. T. Nakamura, Corrected score function for errors-in-variables models: methodology and application to generalized linear models. Biometrika 77 (1990) 127–137. [CrossRef] [MathSciNet] [Google Scholar]
  29. T. Nakamura, Proportional hazards model with covariates subject to measurement error. Biometrics 48 (1992) 829-838. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  30. R.L. Prentice, Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika 69 (1982) 331–342. [CrossRef] [MathSciNet] [Google Scholar]
  31. R.L. Prentice and S.G. Self, Asymptotic distribution theory for Cox-type regression models with general relative risk form. Ann. Statist. 11 (1983) 804–813. [CrossRef] [MathSciNet] [Google Scholar]
  32. O. Reiersøl, Identifiability of a linear relation between variables which are subject to error. Econometrica 18 (1950) 375-389. [CrossRef] [MathSciNet] [Google Scholar]
  33. P. Reynaud-Bouret, Adaptive estimation of the intensity of inhomogeneous Poisson processes via concentration inequalities. Prob. Theory Relat. Fields 126 (2003) 103–153. [Google Scholar]
  34. L.A. Stefanski, Unbiaised estimation of a nonlinear function of a normal mean with application to measurement error models. Commun. Stat. -Theory Meth. 18 (1989) 4335–4358. [CrossRef] [Google Scholar]
  35. M.-L. Taupin, Semi-parametric estimation in the nonlinear structural errors-in-variables model. Ann. Statist. 29 (2001) 66–93. [CrossRef] [MathSciNet] [Google Scholar]
  36. T.T. Tsiatis, V. DeGruttola and M.S. Wulfsohn, Modeling the relationship of survival to longitudinal data measured with error. Application to survival and cd4 counts in patients with aids. J. Amer. Statist. Assoc. 90 (1995) 27–37. [Google Scholar]
  37. Y.N. Tyurin, A. Yakovlev, J. Shi and L. Bass, Testing a model of aging in animal experiments. Biometrics 51 (1995) 363–372. [CrossRef] [PubMed] [Google Scholar]
  38. A.W. van der Vaart and J.A. Wellner, Weak convergences and empirical processes. With applications to statistics. Springer Series in Statistics (1996). [Google Scholar]
  39. S.X. Xie, C.Y. Wang and R.L. Prentice, A risk set calibration method for failure time regression by using a covariate reliability sample. J.R. Stat. Soc., Ser. B, Stat. Methodol. 63 (2001) 855–870. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.