Lifetime Data Analysis

, Volume 19, Issue 1, pp 59–78 | Cite as

A proportional hazards regression model with change-points in the baseline function

  • Abdullah Oueslati
  • Olivier Lopez


In this article, we consider a new regression model for counting processes under a proportional hazards assumption. This model is motivated by the need of understanding the evolution of the booking process of a railway company. The main novelty of the approach consists in assuming that the baseline hazard function is piecewise constant, with unknown times of jump (these times of jump are estimated from the data as model parameters). Hence, the parameters of the model can be separated into two different types: parameters that measure the influence of the covariates, and parameters from a multiple change-point model for the baseline. Cox’s semiparametric regression can be seen as a limit case of our model. We develop an iterative procedure to estimate the different parameters, and a test procedure that allows to perform change-point detection in the baseline. Our technique is supported by simulation studies and a real data analysis, which show that our model can be a reasonable alternative to Cox’s regression model, particularly in the presence of tied event times.


Proportional hazards regression Change-point detection Iterative procedures Dynamic programming Revenue-management Survival analysis Recurrent events 


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Innovation and Research Department, SNCFParis Cedex 12France
  2. 2.Laboratoire de Statistique Théorique et AppliquéeUniversité Pierre et Marie Curie Paris VIParisFrance

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