Free Access
Volume 13, January 2009
Page(s) 87 - 114
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.
  2. P.K. Andersen, O. Borgan, R.D. Gill and N. Keiding, Statistical models based on counting processes. Springer Series in Statistics (1993).
  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]
  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]
  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.
  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).
  7. J.S. Buzas, Unbiased scores in proportional hazards regression with covariate measurement error. J. Statist. Plann. Inference, 67 (1998) 247–257.
  8. R.J. Carroll, D. Ruppert, and L.A. Stefanski, Measurement error in nonlinear models. Chapman and Hall (1995).
  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]
  10. D.R. Cox and D. Oakes, Analysis of survival data. Monographs on Statistics and Applied Probability. Chapman and Hall (1984).
  11. J. Fan and Y.K. Truong, Nonparametric regression with errors in variables. Ann. Statist. 21 (1993) 1900–1925. [CrossRef] [MathSciNet]
  12. M.V. Fedoryuk, Asimptotika: integraly i ryady. Asymptotics: Integrals and Series (1987).
  13. W.A. Fuller, Measurement error models. Wiley Series in Probability and Mathematical Statistics (1987).
  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]
  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]
  16. N.L. Hjort, On inference in parametric survival data models. Int. Stat. Rev. 60 (1992) 355–387. [CrossRef]
  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).
  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]
  19. C. Hu and D.Y. Lin, Cox regression with covariate measurement error. Scand. J. Stat. 29 (2002) 637–655. [CrossRef]
  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]
  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]
  22. F.H. Kong, Adjusting regression attenuation in the Cox proportional hazards model. J. Statist. Plann. Inference 79 (1999) 31–44. [CrossRef] [MathSciNet]
  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]
  24. O.V. Lepski and B.Y. Levit, Adaptive minimax estimation of infinitely differentiable functions. Math. Methods Statist. 7 (1998) 123–156. [MathSciNet]
  25. Y. Li and L. Ryan, Survival analysis with heterogeneous covariate measurement error. J. Amer. Statist. Assoc. 99 (2004) 724-735. [CrossRef] [MathSciNet]
  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]
  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]
  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]
  29. T. Nakamura, Proportional hazards model with covariates subject to measurement error. Biometrics 48 (1992) 829-838. [CrossRef] [MathSciNet] [PubMed]
  30. R.L. Prentice, Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika 69 (1982) 331–342. [CrossRef] [MathSciNet]
  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]
  32. O. Reiersøl, Identifiability of a linear relation between variables which are subject to error. Econometrica 18 (1950) 375-389. [CrossRef] [MathSciNet]
  33. P. Reynaud-Bouret, Adaptive estimation of the intensity of inhomogeneous Poisson processes via concentration inequalities. Prob. Theory Relat. Fields 126 (2003) 103–153. [CrossRef]
  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]
  35. M.-L. Taupin, Semi-parametric estimation in the nonlinear structural errors-in-variables model. Ann. Statist. 29 (2001) 66–93. [CrossRef] [MathSciNet]
  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.
  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]
  38. A.W. van der Vaart and J.A. Wellner, Weak convergences and empirical processes. With applications to statistics. Springer Series in Statistics (1996).
  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.

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.