Estimation in Cox proportional hazards model with heteroscedastic errors in covariates
Volume 11, Issue 4 (2024), pp. 479–489
Pub. online: 30 May 2024
Type: Research Article
Open Access
Received
1 March 2024
1 March 2024
Revised
13 May 2024
13 May 2024
Accepted
14 May 2024
14 May 2024
Published
30 May 2024
30 May 2024
Abstract
Consistent estimators of the baseline hazard rate and the regression parameter are constructed in the Cox proportional hazards model with heteroscedastic measurement errors, assuming that the baseline hazard function belongs to a certain class of functions with bounded Lipschitz constants.
References
Augustin, T.: An exact corrected log-likelihood function for Cox’s proportional hazards model under measurement error and some extensions. Scand. J. Stat. 31(1), 43–50 (2004). MR2042597. https://doi.org/10.1111/j.1467-9469.2004.00371.x
Augustin, T., Döring, A., Rummel, D.: Regression calibration for Cox regression under heteroscedastic measurement error—determining risk factors of cardiovascular diseases from error-prone nutritional replication data. In: Recent Advances in Linear Models and Related Areas, pp. 253–278. Springer (2008). MR2523854. https://doi.org/10.1007/978-3-7908-2064-5_13
Cox, D.R.: Regression models and life-tables. J. Roy. Statist. Soc. Ser. B 34, 187–220 (1972). MR0341758
Durot, C., Lopuhaä, H.P.: Limit theory in monotone function estimation. Statist. Sci. 33(4), 547–567 (2018). MR3881208. https://doi.org/10.1214/18-STS664
Feller, W.: An Introduction to Probability Theory and Its Applications. Vol. I, 509 (1968). MR0228020
Grenander, U.: On the theory of mortality measurement. II. Skand. Aktuarietidskr. 39, 125–1531957 (1956). MR0093415. https://doi.org/10.1080/03461238.1956.10414944
Groeneboom, P., Jongbloed, G.: Some developments in the theory of shape constrained inference. Statist. Sci. 33(4), 473–492 (2018). MR3881204. https://doi.org/10.1214/18-STS657
Kalbfleisch, J.D., Prentice, R.L.: The Statistical Analysis of Failure Time Data vol. 360. John Wiley & Sons (2011) MR0570114
Kong, F.H., Gu, M.: Consistent estimation in Cox proportional hazards model with covariate measurement errors. Statist. Sinica 9(4), 953–969 (1999). MR1744820
Kukush, A., Baran, S., Fazekas, I., Usoltseva, E.: Simultaneous estimation of baseline hazard rate and regression parameters in Cox proportional hazards model with measurement error. J. Statist. Res. 45(2), 77–94 (2011). MR2934363
Kukush, O.G., Chernova, O.O.: Consistent estimation in the Cox proportional hazards model with measurement errors under an unboundedness condition for the parameter set. Teor. Ĭmovı¯r. Mat. Stat. 96, 100–109 (2017). MR3666874. https://doi.org/10.1090/tpms/1036
Lawless, J.F.: Statistical Models and Methods for Lifetime Data vol. 362. John Wiley & Sons (2011) MR0640866
Lopuhaä, H.P., Nane, G.F.: Shape constrained non-parametric estimators of the baseline distribution in Cox proportional hazards model. Scand. J. Stat. 40(3), 619–646 (2013). MR3091700. https://doi.org/10.1002/sjos.12008
Qin, J., Deng, G., Ning, J., Yuan, A., Shen, Y.: Estrogen receptor expression on breast cancer patients’ survival under shape-restricted Cox regression model. Ann. Appl. Stat. 15(3), 1291–1307 (2021). MR4316649. https://doi.org/10.1214/21-aoas1446
Samworth, R.J.: Recent progress in log-concave density estimation. Statist. Sci. 33(4), 493–509 (2018). MR3881205. https://doi.org/10.1214/18-STS666
Wallace, M.: Analysis in an imperfect world. Significance 17(1), 14–19 (2020). MR4446481