.. Comparaison-entre-l-'estimateur-de-kaplan-meier, ligne pleine) et l'estimateur paramétrique (ligne pointillée) de la fonction de survie. La ligne en tirets représente la vraie courbe de survie, p.56

T. Survie-empirique-de, ligne continue) obtenue avec les tables de population slovénienne Estimation paramétrique de S (ligne en tirets) Le temps est exprimé en jours depuis l'année 1982, p.63

O. Aalen, A Model for Nonparametric Regression Analysis of Counting Processes, Mathematical statistics and probability theory, pp.1-25, 1980.
DOI : 10.1007/978-1-4615-7397-5_1

M. Abrahamowicz, T. Mackenzie, and J. M. Esdaile, Time-Dependent Hazard Ratio: Modeling and Hypothesis Testing with Application in Lupus Nephritis, Journal of the American Statistical Association, vol.18, issue.436, pp.911432-1439, 1996.
DOI : 10.1080/01621459.1984.10478092

P. K. Andersen and R. D. Gill, Cox's regression model for counting processes : A large sample study. The Annals of Statistics, pp.1100-1120, 1982.
DOI : 10.1214/aos/1176345976

J. A. Anderson and A. Senthilselvan, A Two-Step Regression Model for Hazard Functions, Applied Statistics, vol.31, issue.1, pp.44-51, 1982.
DOI : 10.2307/2347073

A. Antoniadis, G. Grégoire, and G. Nason, Density and hazard rate estimation for right-censored data by using wavelet methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.1, pp.6163-84, 1999.
DOI : 10.1111/1467-9868.00163

E. Arjas, A Graphical Method for Assessing Goodness of Fit in Cox's Proportional Hazards Model, Journal of the American Statistical Association, vol.79, issue.401, pp.204-212, 1988.
DOI : 10.1080/01621459.1988.10478588

D. Azriel, M. Mandel, R. , and Y. , The treatment versus experimentation dilemma in dose finding studies, Journal of Statistical Planning and Inference, vol.141, issue.8, pp.141-2759, 2011.
DOI : 10.1016/j.jspi.2011.03.001

V. Bagdonavi?ius and M. Nikulin, Asymptotical Analysis of Semiparametric Models in Survival Analysis and Accelerated Life Testing, Statistics, vol.4, issue.3, pp.261-283, 1997.
DOI : 10.2307/2347465

V. Bagdonavi?ius and M. Nikulin, Mod??le statistique de d??gradation avec des covariables d??pendant du temps, Comptes Rendus de l'Académie des Sciences-Series I-Mathematics, pp.131-134, 2000.
DOI : 10.1016/S0764-4442(00)00107-5

J. Bai, LEAST SQUARES ESTIMATION OF A SHIFT IN LINEAR PROCESSES, Journal of Time Series Analysis, vol.72, issue.5, pp.453-472, 1994.
DOI : 10.1214/aos/1176350509

J. Bai, Estimation of a Change Point in Multiple Regression Models, Review of Economics and Statistics, vol.74, issue.4, pp.551-563, 1997.
DOI : 10.1214/aos/1176350509

J. Bai and P. Perron, Computation and analysis of multiple structural change models, Journal of Applied Econometrics, vol.6, issue.1, pp.1-22, 2003.
DOI : 10.1002/jae.659

A. Barron, L. Birgé, and P. Massart, Risk bounds for model selection via penalization . Probability theory and related fields, pp.301-413, 1999.
DOI : 10.1007/s004400050210

R. Beran, Nonparametric regression with randomly censored survival data, 1981.

G. Biau, Analysis of a random forests model, Journal of Machine Learning Research, vol.13, pp.1063-1095, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00476545

G. Biau, L. Devroye, and G. Lugosi, Consistency of random forests and other averaging classifiers, Journal of Machine Learning Research, vol.9, pp.2015-2033, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00355368

L. Birgé and P. Massart, From Model Selection to Adaptive Estimation, 1997.
DOI : 10.1007/978-1-4612-1880-7_4

T. M. Braun and N. Jia, A Generalized Continual Reassessment Method for Two-Agent Phase I Trials, Statistics in Biopharmaceutical Research, vol.65, issue.2, pp.105-115, 2013.
DOI : 10.1080/19466315.2013.767213

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891671

T. M. Braun and S. Wang, A Hierarchical Bayesian Design for Phase I Trials of Novel Combinations of Cancer Therapeutic Agents, Biometrics, vol.27, issue.3, pp.805-812, 2010.
DOI : 10.1111/j.1541-0420.2009.01363.x

L. Breiman, Random forests, Machine Learning, pp.5-32, 2001.

L. Breiman, Consistency for a simple model of random forests, UC Berkeley, 2004.

L. Breiman, J. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, 1984.

C. C. Brown, On the Use of Indicator Variables for Studying the Time-Dependence of Parameters in a Response-Time Model, Biometrics, vol.31, issue.4, pp.863-872, 1975.
DOI : 10.2307/2529811

Z. Cai and Y. Sun, Local Linear Estimation for Time-Dependent Coefficients in Cox's Regression Models, Scandinavian Journal of Statistics, vol.18, issue.1, pp.93-111, 2003.
DOI : 10.1016/0197-2456(81)90005-2

G. Castellan and F. Letué, Estimation of the cox regression function via model selection. Chapter, 2000.

C. Chauvel, Empirical Processes for Inference in the Non-Proportional Hazards model, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01127777

C. Chauvel, O. , and J. , Tests for comparing estimated survival functions, Biometrika, vol.101, issue.3, p.15, 2014.
DOI : 10.1093/biomet/asu015

Y. K. Cheung, Dose finding by the continual reassessment method, 2011.
DOI : 10.1201/b10783

Y. Cheung and . Kuen, Coherence principles in dose-finding studies, Biometrika, vol.92, issue.4, pp.863-873, 2005.
DOI : 10.1093/biomet/92.4.863

M. Clertant, Semi-parametric bayesian model, applications in dose finding studies, 2015.

F. Comte, S. Gaïffas, and A. Guilloux, Adaptive estimation of the conditional intensity of marker-dependent counting processes, Annales de l'institut Henri Poincaré, pp.1171-1196, 2011.
DOI : 10.1214/10-AIHP386

URL : https://hal.archives-ouvertes.fr/hal-00333356

D. R. Cox, Regression Models and Life-Tables, Journal of the Royal Statistical Society. Series B (Methodological), vol.34, issue.2, pp.187-220, 1972.
DOI : 10.1007/978-1-4612-4380-9_37

D. M. Dabrowska, Uniform consistency of the kernel conditional kaplan-meier estimate. The Annals of Statistics, pp.1157-1167, 1989.

R. B. Davies, Hypothesis testing when a nuisance parameter is present only under the alternative, Biometrika, vol.64, issue.2, pp.247-254, 1977.
DOI : 10.1093/biomet/64.2.247

M. Denil, D. Matheson, N. Freitas, and . De, Consistency of online random forests, 2013.

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, 1996.
DOI : 10.1007/978-1-4612-0711-5

B. Efron, The Efficiency of Cox's Likelihood Function for Censored Data, Journal of the American Statistical Association, vol.22, issue.359, pp.72557-565, 1977.
DOI : 10.2307/2344317

B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap. Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 1994.
DOI : 10.1007/978-1-4899-4541-9

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

T. R. Fleming and D. P. Harrington, Counting processes and survival analysis, 2011.
DOI : 10.1002/9781118150672

E. A. Gehan, A generalized Wilcoxon test for comparing arbitrarily singly-censored samples, Biometrika, vol.52, issue.1-2, pp.203-223, 1965.
DOI : 10.1093/biomet/52.1-2.203

R. Genuer, J. Poggi, and C. Tuleau, Random forests : some methodological insights, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00340725

P. M. Grambsch and T. M. Therneau, Proportional hazards tests and diagnostics based on weighted residuals, Biometrika, vol.81, issue.3, pp.515-526, 1994.
DOI : 10.1093/biomet/81.3.515

R. J. Gray, Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis, Journal of the American Statistical Association, vol.36, issue.420, pp.942-951, 1992.
DOI : 10.1214/aos/1176347503

P. Haara, A note on the asymptotic behaviour of the empirical score in cox's regression model for counting processes, Proceedings of the 1st World Congress of the Bernoulli Society, pp.139-142, 1987.

D. P. Harrington and T. R. Fleming, A class of rank test procedures for censored survival data, Biometrika, vol.69, issue.3, pp.553-566, 1982.
DOI : 10.1093/biomet/69.3.553

T. Hastie and R. Tibshirani, Exploring the Nature of Covariate Effects in the Proportional Hazards Model, Biometrics, vol.46, issue.4, pp.1005-1016, 1990.
DOI : 10.2307/2532444

T. Hastie and R. Tibshirani, Varying-coefficient models, Journal of the Royal Statistical Society. Series B (Methodological), vol.55, issue.4, pp.757-796, 1993.

D. M. Hawkins, Fitting multiple change-point models to data, Computational Statistics & Data Analysis, vol.37, issue.3, pp.323-341, 2001.
DOI : 10.1016/S0167-9473(00)00068-2

K. R. Hess, Assessing time-by-covariate interactions in proportional hazards regression models using cubic spline functions, Statistics in Medicine, vol.87, issue.10, pp.1045-1062, 1994.
DOI : 10.1002/sim.4780131007

T. Hothorn and B. Lausen, On the exact distribution of maximally selected rank statistics, Computational Statistics & Data Analysis, vol.43, issue.2, pp.121-137, 2003.
DOI : 10.1016/S0167-9473(02)00225-6

X. Huang, S. Biswas, Y. Oki, J. Issa, and D. A. Berry, A Parallel Phase I/II Clinical Trial Design for Combination Therapies, Biometrics, vol.64, issue.2, pp.429-436, 2007.
DOI : 10.1111/j.1541-0420.2006.00685.x

C. Huber, Censored and truncated lifetime data. Recent Advances in Reliability Theory, pp.291-305, 2000.
DOI : 10.1007/978-1-4612-1384-0_19

H. Ishwaran, Variable importance in binary regression trees and forests, Electronic Journal of Statistics, vol.1, issue.0, pp.519-537, 2007.
DOI : 10.1214/07-EJS039

URL : http://arxiv.org/abs/0711.2434

H. Ishwaran and U. B. Kogalur, Consistency of random survival forests, Statistics & Probability Letters, vol.80, issue.13-14, pp.1056-1064, 2010.
DOI : 10.1016/j.spl.2010.02.020

H. Ishwaran, U. B. Kogalur, E. H. Blackstone, and M. S. Lauer, Random survival forests, The Annals of Applied Statistics, vol.2, issue.3, pp.841-860, 2008.
DOI : 10.1214/08-AOAS169

J. D. Kalbfleisch and R. L. Prentice, Marginal likelihoods based on Cox's regression and life model, Biometrika, vol.60, issue.2, pp.267-278, 1973.
DOI : 10.1093/biomet/60.2.267

J. D. Kalbfleisch and R. L. Prentice, The statistical analysis of failure time data. Wiley series in probability and mathematical statistics : Applied probability and statistics

J. D. Kalbfleisch and R. L. Prentice, The statistical analysis of failure time data, 2011.
DOI : 10.1002/9781118032985

E. L. Kaplan and P. Meier, Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association, vol.37, issue.282, pp.457-481, 1958.
DOI : 10.1214/aoms/1177731566

R. Kay, Proportional Hazard Regression Models and the Analysis of Censored Survival Data, Applied Statistics, vol.26, issue.3, pp.227-237, 1977.
DOI : 10.2307/2346962

C. Kleiber, K. Hornik, F. Leisch, and A. Zeileis, strucchange : An r package for testing for structural change in linear regression models, Journal of Statistical Software, vol.7, issue.2, pp.1-38, 2002.

J. P. Klein and M. L. Moeschberger, Survival analysis : techniques for censored and truncated data, 2003.
DOI : 10.1007/978-1-4757-2728-9

J. P. Klein, H. C. Van-houwelingen, J. G. Ibrahim, and T. Scheike, Handbook of survival analysis, 2013.

B. Lausen and M. Schumacher, Evaluating the effect of optimized cutoff values in the assessment of prognostic factors, Computational Statistics & Data Analysis, vol.21, issue.3, pp.307-326, 1996.
DOI : 10.1016/0167-9473(95)00016-X

J. Lawless, Statistical models and methods for lifetime data, 2011.
DOI : 10.1002/9781118033005

M. Leblanc and J. Crowley, Survival Trees by Goodness of Split, Journal of the American Statistical Association, vol.74, issue.422, pp.457-467, 1993.
DOI : 10.1093/biomet/77.1.147

M. Leblanc and J. Crowley, Adaptive Regression Splines in the Cox Model, Biometrics, vol.77, issue.1, pp.204-213, 1999.
DOI : 10.1111/j.0006-341X.1999.00204.x

S. Leurgans, Three classes of censored data rank tests: Strengths and weaknesses under censoring, Biometrika, vol.70, issue.3, pp.651-658, 1983.
DOI : 10.1093/biomet/70.3.651

S. Leurgans, Asymptotic behavior of two-sample rank tests in the presence of random censoring. The Annals of Statistics, pp.572-589, 1984.

K. Y. Liang, S. G. Self, and X. Liu, The Cox Proportional Hazards Model with Change Point: An Epidemiologic Application, Biometrics, vol.46, issue.3, pp.783-793, 1990.
DOI : 10.2307/2532096

A. Liaw and M. Wiener, Classification and regression by randomforest, pp.18-22, 2002.

D. Y. Lin, Goodness-of-Fit Analysis for the Cox Regression Model Based on a Class of Parameter Estimators, Journal of the American Statistical Association, vol.34, issue.415, pp.725-728, 1991.
DOI : 10.1093/biomet/64.1.156

D. Y. Lin and Z. Ying, Semiparametric analysis of general additive-multiplicative hazard models for counting processes. The annals of Statistics, pp.1712-1734, 1995.

J. Liu, S. Wu, and J. V. Zidek, On segmented multivariate regression, Statistica Sinica, vol.7, issue.2, pp.497-525, 1997.

A. P. Mander and M. J. Sweeting, A product of independent beta probabilities dose escalation design for dual-agent phase I trials, Statistics in Medicine, vol.25, issue.12, pp.1261-1276, 2015.
DOI : 10.1002/sim.6434

N. Mantel, Evaluation of survival data and two new rank order statistics arising in its consideration, Cancer chemotherapy reports. Part, vol.1, issue.503, pp.163-170, 1966.

E. Marubini and M. G. Valsecchi, Analysing survival data from clinical trials and observational studies, 2004.

L. Marzec and P. Marzec, On fitting Cox's regression model with time-dependent coefficients, Biometrika, vol.84, issue.4, pp.901-908, 1997.
DOI : 10.1093/biomet/84.4.901

I. W. Mckeague and K. J. Utikal, Inference for a nonlinear counting process regression model. The Annals of Statistics, pp.1172-1187, 1990.

L. Meier, S. Van-geerde, and P. Bühlmann, High-dimensional additive modeling, The Annals of Statistics, vol.37, issue.6B, pp.3779-3821, 2009.
DOI : 10.1214/09-AOS692

URL : http://arxiv.org/abs/0806.4115

L. Mentch and G. Hooker, Ensemble trees and clts : Statistical inference for supervised learning, 2014.

T. Moreau, J. O. Quigley, and M. Mesbah, A Global Goodness-of-Fit Statistic for the Proportional Hazards Model, Applied Statistics, vol.34, issue.3, pp.212-218, 1985.
DOI : 10.2307/2347465

S. A. Murphy and P. K. Sen, Time-dependent coefficients in a Cox-type regression model, Stochastic Processes and their Applications, pp.153-180, 1991.
DOI : 10.1016/0304-4149(91)90039-F

D. Naftel, E. Blackstone, and M. Turner, Conservation of events, 1985.

O. Quigley and J. , Khmaladze-type graphical evaluation of the proportional hazards assumption, Biometrika, vol.90, issue.3, pp.577-584, 2003.
DOI : 10.1093/biomet/90.3.577

O. Quigley and J. , Proportional hazards regression, 2008.

O. Quigley, J. Pessione, and F. , Score Tests for Homogeneity of Regression Effect in the Proportional Hazards Model, Biometrics, vol.45, issue.1, pp.135-144, 1989.
DOI : 10.2307/2532040

O. Quigley, J. Pessione, and F. , The problem of a covariate-time qualitative interaction in a survival study, Biometrics, issue.1, pp.47101-115, 1991.

O. Quigley, J. , M. Pepe, and L. Fisher, Continual Reassessment Method: A Practical Design for Phase 1 Clinical Trials in Cancer, Biometrics, vol.46, issue.1, pp.33-48, 1990.
DOI : 10.2307/2531628

O. Sullivan and F. , Nonparametric estimation in the cox model. The Annals of Statistics, pp.124-145, 1993.

R. Peto and J. Peto, Asymptotically Efficient Rank Invariant Test Procedures, Journal of the Royal Statistical Society. Series A (General), vol.135, issue.2, pp.185-207, 1972.
DOI : 10.2307/2344317

M. Pohar and J. Stare, Relative survival analysis in r. Computer methods and programs in biomedicine, pp.272-278, 2006.
DOI : 10.1016/j.cmpb.2006.01.004

Y. Qi, Ensemble Machine Learning, chapter Random forest for bioinformatics, pp.307-323, 2012.
DOI : 10.1007/978-1-4419-9326-7_11

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

P. Reynaud-bouret, Penalized projection estimators of the Aalen multiplicative intensity, Bernoulli, vol.12, issue.4, pp.633-661, 2006.
DOI : 10.3150/bj/1155735930

G. Rogez, J. Rihan, S. Ramalingam, C. Orrite, T. et al., Randomized trees for human pose detection, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587617

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

G. A. Satten and S. Datta, The kaplan?meier estimator as an inverse-probabilityof-censoring weighted average. The American Statistician, pp.207-210, 2001.
DOI : 10.1198/000313001317098185

T. H. Scheike and M. Zhang, An Additive-Multiplicative Cox-Aalen Regression Model, Scandinavian Journal of Statistics, vol.93, issue.1, pp.75-88, 2002.
DOI : 10.1214/aos/1031594730

E. Scornet, On the asymptotics of random forests. arXiv preprint arXiv :1409, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01061506

E. Scornet, G. Biau, and J. Vert, Consistency of random forests, The Annals of Statistics, vol.43, issue.4, 2014.
DOI : 10.1214/15-AOS1321SUPP

URL : https://hal.archives-ouvertes.fr/hal-00990008

M. R. Segal, Regression Trees for Censored Data, Biometrics, vol.44, issue.1, pp.35-47, 1988.
DOI : 10.2307/2531894

L. Z. Shen, O. Quigley, and J. , Consistency of continual reassessment method under model misspecification, Biometrika, vol.83, issue.2, pp.395-405, 1996.
DOI : 10.1093/biomet/83.2.395

J. M. Skolnik, J. S. Barrett, B. Jayaraman, D. Patel, and P. C. Adamson, Shortening the Timeline of Pediatric Phase I Trials: The Rolling Six Design, Journal of Clinical Oncology, vol.26, issue.2, pp.190-195, 2008.
DOI : 10.1200/JCO.2007.12.7712

D. M. Stablein, W. H. Carter, and J. W. Novak, Analysis of survival data with nonproportional hazard functions, Controlled Clinical Trials, vol.2, issue.2, pp.149-159, 1981.
DOI : 10.1016/0197-2456(81)90005-2

C. J. Stone, Optimal rates of convergence for nonparametric estimators. The Annals of Statistics, pp.1348-1360, 1980.

C. J. Stone, Optimal global rates of convergence for nonparametric regression. The Annals of Statistics, pp.1040-1053, 1982.

B. E. Storer, Design and Analysis of Phase I Clinical Trials, Biometrics, vol.45, issue.3, pp.925-937, 1989.
DOI : 10.2307/2531693

C. A. Struthers and J. D. Kalbfleisch, Misspecified proportional hazard models, Biometrika, vol.73, issue.2, pp.363-369, 1986.
DOI : 10.1093/biomet/73.2.363

W. Stute, Conditional empirical processes. The Annals of Statistics, pp.638-647, 1986.

W. Stute, The central limit theorem under random censorship. The Annals of Statistics, pp.422-439, 1995.
DOI : 10.1214/aos/1176324528

URL : http://anson.ucdavis.edu/~wang/Bernoulli_CLTtrun.pdf

W. Stute and J. Wang, The Strong Law under Random Censorship, The Annals of Statistics, vol.21, issue.3, pp.1591-1607, 1993.
DOI : 10.1214/aos/1176349273

URL : http://projecteuclid.org/download/pdf_1/euclid.aos/1176349273

J. H. Sullivan, Estimating the locations of multiple change points in the mean, Computational Statistics, vol.17, issue.2, pp.289-296, 2002.
DOI : 10.1007/s001800200107

V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan et al., Random Forest:??? A Classification and Regression Tool for Compound Classification and QSAR Modeling, Journal of Chemical Information and Computer Sciences, vol.43, issue.6, pp.1947-1958, 2003.
DOI : 10.1021/ci034160g

P. F. Thall, R. E. Millikan, P. Mueller, L. , and S. , Dose-Finding with Two Agents in Phase I Oncology Trials, Biometrics, vol.55, issue.3, pp.59487-496, 2003.
DOI : 10.1111/1541-0420.00058

T. M. Therneau and P. M. Grambsch, Modeling survival data : extending the Cox model, 2000.
DOI : 10.1007/978-1-4757-3294-8

M. Laan, E. C. Van-der, . Polley, and A. E. Hubbard, Super learner, Statistical Applications in Genetics and Molecular Biology, vol.6, 2007.

P. Verweij, H. Houwelingen, and . Van, Time-Dependent Effects of Fixed Covariates in Cox Regression, Biometrics, vol.51, issue.4, pp.1550-1556, 1995.
DOI : 10.2307/2533286

S. Wager, Asymptotic theory for random forests, 2014.

N. A. Wages, M. R. Conaway, O. Quigley, and J. , Continual Reassessment Method for Partial Ordering, Biometrics, vol.27, issue.4, pp.1555-1563, 2011.
DOI : 10.1111/j.1541-0420.2011.01560.x

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3141101

G. Wahba, Spline models for observational data, Siam, vol.59, 1990.
DOI : 10.1137/1.9781611970128

K. Wang and A. Ivanova, Two-Dimensional Dose Finding in Discrete Dose Space, Biometrics, vol.14, issue.1, pp.217-222, 2005.
DOI : 10.1111/1541-0420.00058

L. J. Wei, Testing Goodness of Fit for Proportional Hazards Model with Censored Observations, Journal of the American Statistical Association, vol.9, issue.387, pp.79649-652, 1984.
DOI : 10.1080/01621459.1984.10478092

R. Xu and S. Adak, Survival Analysis with Time-Varying Regression Effects Using a Tree-Based Approach, Biometrics, vol.18, issue.2, pp.305-315, 2002.
DOI : 10.1111/j.0006-341X.2002.00305.x

R. Xu and D. P. Harrington, A Semiparametric Estimate of Treatment Effects with Censored Data, Biometrics, vol.1, issue.3, pp.875-885, 2001.
DOI : 10.1111/j.0006-341X.2001.00875.x

R. Xu, O. Quigley, and J. , Estimating average regression effect under non-proportional hazards, Biostatistics, vol.1, issue.4, pp.423-439, 2000.
DOI : 10.1093/biostatistics/1.4.423

F. Yang, J. Wang, F. , and G. , Kernel induced random survival forests. arXiv preprint, 2010.

S. Yang and R. Prentice, Semiparametric analysis of short-term and long-term hazard ratios with two-sample survival data, Biometrika, vol.92, issue.1, pp.1-17, 2005.
DOI : 10.1093/biomet/92.1.1

G. Yin and Y. Yuan, A Latent Contingency Table Approach to Dose Finding for Combinations of Two Agents, Biometrics, vol.63, issue.3, pp.866-875, 2009.
DOI : 10.1111/j.1541-0420.2008.01119.x

G. Yin and Y. Yuan, Bayesian dose finding in oncology for drug combinations by copula regression, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.14, issue.2, pp.211-224, 2009.
DOI : 10.1111/j.1467-9876.2009.00649.x

A. Zeileis, C. Kleiber, W. Krämer, and K. Hornik, Testing and dating of structural changes in practice, Computational Statistics & Data Analysis, vol.44, issue.1-2, pp.109-123, 2003.
DOI : 10.1016/S0167-9473(03)00030-6

D. M. Zucker and A. F. Karr, Nonparametric Survival Analysis with Time-Dependent Covariate Effects: A Penalized Partial Likelihood Approach, The Annals of Statistics, vol.18, issue.1, pp.329-353, 1990.
DOI : 10.1214/aos/1176347503