Pons, Odile Estimation in a Cox regression model with a change-point according to a threshold in a covariate. (English) Zbl 1040.62090 Ann. Stat. 31, No. 2, 442-463 (2003). Summary: We consider a nonregular Cox model for independent and identically distributed right censored survival times, with a change-point according to the unknown threshold of a covariate. The maximum partial likelihood estimators of the parameters and the estimator of the baseline cumulative hazard are studied. We prove that the estimator of the change-point is \(n\)-consistent and the estimators of the regression parameters are \(n^{1/2}\)-consistent, and we establish the asymptotic distributions of the estimators. The estimators of the regression parameters and of the baseline cumulative hazard are adaptive in the sense that they do not depend on the knowledge of the change-point. Cited in 28 Documents MSC: 62N02 Estimation in survival analysis and censored data 62E20 Asymptotic distribution theory in statistics 62F12 Asymptotic properties of parametric estimators 62G05 Nonparametric estimation 62M09 Non-Markovian processes: estimation 60F05 Central limit and other weak theorems Keywords:hazard function; right censoring × Cite Format Result Cite Review PDF Full Text: DOI References: [1] ANDERSEN, P. K., BORGAN, Ø., GILL, R. D. and KEIDING, N. (1993). Statistical Models Based on Counting Processes. Springer, New York. · Zbl 0769.62061 [2] ANDERSEN, P. K. and GILL, R. D. (1982). Cox’s regression model for counting processes: A large sample study. Ann. Statist. 10 1100-1120. · Zbl 0526.62026 · doi:10.1214/aos/1176345976 [3] BAILEY, K. R. (1983). The asy mptotic joint distribution of regression and survival parameter estimates in the Cox regression model. Ann. Statist. 11 39-48. · Zbl 0509.62015 · doi:10.1214/aos/1176346054 [4] BILLINGSLEY, P. (1968). Convergence of Probability Measures. Wiley, New York. · Zbl 0172.21201 [5] BRESLOW, N. E. (1972). Discussion of ”Regression model and life-tables,” by D. R. Cox. J. Roy. Statist. Soc. Ser. B 34 216-217. JSTOR: [6] COX, D. R. (1972). Regression model and life-tables (with discussion). J. Roy. Statist. Soc. Ser. B 34 187-220. JSTOR: · Zbl 0243.62041 [7] COX, D. R. (1975). Partial likelihood. Biometrika 62 269-276. JSTOR: · Zbl 0312.62002 · doi:10.1093/biomet/62.2.269 [8] CSÖRG O, M. and HORVÁTH, L. (1997). Limit Theorems in Change-Point Analy sis. Wiley, New York. [9] IBRAGIMOV, I. and HAS’MINSKII, R. (1981). Statistical Estimation: Asy mptotic Theory. Springer, New York. [10] JESPERSEN, N. C. B. (1986). Dichotomizing a continuous covariate in the Cox model. Research Report 86/02, Statistical Research Unit, Univ. Copenhagen. [11] KLEINBAUM, D. G. (1996). Survival Analy sis: A Self-Learning Text. Springer, New York. [12] KUTOy ANTS, Y. A. (1984). Parameter Estimation for Stochastic Processes. Heldermann, Berlin. · Zbl 0542.62073 [13] KUTOy ANTS, Y. A. (1998). Statistical Inference for Spatial Poisson Processes. Lecture Notes in Statist. 134. Springer, New York. · Zbl 0904.62108 [14] LENGLART, E. (1977). Relation de domination entre deux processus. Ann. Inst. H. Poincaré Sect. B (N. S.) 13 171-179. · Zbl 0373.60054 [15] LIANG, K.-Y., SELF, S. and LIU, X. (1990). The Cox proportional hazards model with change point: An epidemiologic application. Biometrics 46 783-793. [16] LUO, X. (1996). The asy mptotic distribution of MLE of treatment lag threshold. J. Statist. Plann. Inference 53 33-61. · Zbl 0856.62022 · doi:10.1016/0378-3758(95)00142-5 [17] LUO, X. and BOy ETT, J. (1997). Estimation of a threshold parameter in Cox regression. Comm. Statist. Theory Methods 26 2329-2346. · Zbl 0954.62571 · doi:10.1080/03610929708832051 [18] LUO, X., TURNBULL, B. and CLARK, L. (1997). Likelihood ratio tests for a change point with survival data. Biometrika 84 555-565. JSTOR: · Zbl 0888.62016 · doi:10.1093/biomet/84.3.555 [19] MATTHEWS, D. E., FAREWELL, V. T. and Py KE, R. (1985). Asy mptotic score-statistic processes and tests for constant hazard against a change-point alternative. Ann. Statist. 13 583-591. · Zbl 0576.62032 · doi:10.1214/aos/1176349540 [20] NÆS, T. (1982). The asy mptotic distribution of the estimator for the regression parameter in Cox’s regression model. Scand. J. Statist. 9 107-115. · Zbl 0488.62049 [21] NGUy EN, H. T., ROGERS, G. S. and WALKER, E. A. (1984). Estimation in change-point hazard rate models. Biometrika 71 299-304. JSTOR: · Zbl 0561.62092 · doi:10.1093/biomet/71.2.299 [22] POLLARD, D. (1989). Asy mptotics via empirical processes (with discussion). Statist. Sci. 4 341-366. · Zbl 0955.60517 · doi:10.1214/ss/1177012394 [23] PONS, O. (2002). Estimation in a Cox regression model with a change-point at an unknown time. Statistics 36 101-124. · Zbl 1009.62082 · doi:10.1080/02331880212043 [24] PONS, O. and DE TURCKHEIM, E. (1988). Cox’s periodic regression model. Ann. Statist. 16 678- 693. · Zbl 0664.62099 · doi:10.1214/aos/1176350828 [25] PRENTICE, R. L. and SELF, S. G. (1983). Asy mptotic distribution theory for Cox-ty pe regression models with general relative risk form. Ann. Statist. 11 804-813. · Zbl 0526.62017 · doi:10.1214/aos/1176346247 [26] REBOLLEDO, R. (1980). Central limit theorems for local martingales. Z. Wahrsch. Verw. Gebiete 51 269-286. · Zbl 0432.60027 · doi:10.1007/BF00587353 [27] TSIATIS, A. A. (1981). A large sample study of Cox’s regression model. Ann. Statist. 9 93-108. · Zbl 0455.62019 · doi:10.1214/aos/1176345335 [28] VAN DER VAART, A. and WELLNER, J. (1996). Weak Convergence and Empirical Processes. Springer, New York. · Zbl 0862.60002 [29] YAO, Y.-C. (1986). Maximum likelihood estimation in hazard rate models with a change-point. Comm. Statist. Theory Methods 15 2455-2466. · Zbl 0606.62109 · doi:10.1080/03610928608829261 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.