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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-01

AUTHORS

Abdullah Oueslati, Olivier Lopez

ABSTRACT

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. More... »

PAGES

59-78

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10985-012-9231-7

DOI

http://dx.doi.org/10.1007/s10985-012-9231-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015963701

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23054240


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