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Semiparametric Inference for Complex and Structural Models in Survival Analysis

Total Cost €


EC-Contrib. €






Project "COSMOS" data sheet

The following table provides information about the project.


Organization address
address: OUDE MARKT 13
city: LEUVEN
postcode: 3000

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Belgium [BE]
 Total cost 2˙318˙750 €
 EC max contribution 2˙318˙750 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-AdG
 Funding Scheme ERC-ADG
 Starting year 2016
 Duration (year-month-day) from 2016-09-01   to  2021-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KATHOLIEKE UNIVERSITEIT LEUVEN BE (LEUVEN) coordinator 2˙250˙000.00


 Project objective

In survival analysis investigators are interested in modeling and analysing the time until an event happens. It often happens that the available data are right censored, which means that only a lower bound of the time of interest is observed. This feature complicates substantially the statistical analysis of this kind of data. The aim of this project is to solve a number of open problems related to time-to-event data, that would represent a major step forward in the area of survival analysis.

The project has three objectives:

[1] Cure models take into account that a certain fraction of the subjects under study will never experience the event of interest. Because of the complex nature of these models, many problems are still open and rigorous theory is rather scarce in this area. Our goal is to fill this gap, which will be a challenging but important task.

[2] Copulas are nowadays widespread in many areas in statistics. However, they can contribute more substantially to resolving a number of the outstanding issues in survival analysis, such as in quantile regression and dependent censoring. Finding answers to these open questions, would open up new horizons for a wide variety of problems.

[3] We wish to develop new methods for doing correct inference in some of the common models in survival analysis in the presence of endogeneity or measurement errors. The present methodology has serious shortcomings, and we would like to propose, develop and validate new methods, that would be a major breakthrough if successful.

The above objectives will be achieved by using mostly semiparametric models. The development of mathematical properties under these models is often a challenging task, as complex tools from the theory on empirical processes and semiparametric efficiency are required. The project will therefore require an innovative combination of highly complex mathematical skills and cutting edge results from modern theory for semiparametric models.


year authors and title journal last update
List of publications.
2017 Majda Talamakrouni, Anouar El Ghouch, Ingrid Van Keilegom
Parametrically guided local quasi-likelihood with censored data
published pages: 2773-2799, ISSN: 1935-7524, DOI: 10.1214/17-ejs1293
Electronic Journal of Statistics 11/2 2020-04-15
2017 A. Bertrand, C. Legrand, D. Léonard, I. Van Keilegom
Robustness of estimation methods in a survival cure model with mismeasured covariates
published pages: 3-18, ISSN: 0167-9473, DOI: 10.1016/j.csda.2016.11.013
Computational Statistics & Data Analysis 113 2020-04-15
2018 Lan Wang, Ingrid Van Keilegom, Adam Maidman
Wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors
published pages: 859-872, ISSN: 0006-3444, DOI: 10.1093/biomet/asy037
Biometrika 105/4 2020-04-15
2017 Aurelie Bertrand, Catherine Legrand, Raymond J. Carroll, Christophe de Meester, Ingrid Van Keilegom
Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach
published pages: asw054, ISSN: 0006-3444, DOI: 10.1093/biomet/asw054
Biometrika 2020-04-15
2018 Aleksandar Sujica, Ingrid Van Keilegom
The copula-graphic estimator in censored nonparametric location-scale regression models
published pages: 89-114, ISSN: 2452-3062, DOI: 10.1016/j.ecosta.2017.07.002
Econometrics and Statistics 7 2020-04-15
2018 U U Müller, I Van Keilegom
Goodness-of-fit tests for the cure rate in a mixture cure model
published pages: 211-227, ISSN: 0006-3444, DOI: 10.1093/biomet/asy058
Biometrika 106/1 2020-04-15
2018 Nils Lid Hjort, Ian McKeague, Ingrid Van Keilegom
Hybrid combinations of parametric and empirical likelihoods
published pages: , ISSN: 1017-0405, DOI: 10.5705/ss.202017.0291
Statistica Sinica 2020-04-15
2017 Benjamin Colling, Ingrid Van Keilegom
Goodness-of-fit tests in semiparametric transformation models using the integrated regression function
published pages: 10-30, ISSN: 0047-259X, DOI: 10.1016/j.jmva.2017.05.006
Journal of Multivariate Analysis 160 2020-04-15
2019 Anne Vanhems, Ingrid Van Keilegom
published pages: 73-110, ISSN: 0266-4666, DOI: 10.1017/s0266466618000026
Econometric Theory 35/1 2020-04-15
2019 Enno Mammen, Ingrid Van Keilegom, Kyusang Yu
Expansion for moments of regression quantiles with applications to nonparametric testing
published pages: 793-827, ISSN: 1350-7265, DOI: 10.3150/17-bej986
Bernoulli 25/2 2020-04-15
2018 Juan Carlos Escanciano, Juan Carlos Pardo-Fernández, Ingrid Van Keilegom
Asymptotic distribution-free tests for semiparametric regressions with dependent data
published pages: 1167-1196, ISSN: 0090-5364, DOI: 10.1214/17-aos1581
The Annals of Statistics 46/3 2020-04-15
2018 Maïlis Amico, Ingrid Van Keilegom
Cure Models in Survival Analysis
published pages: 311-342, ISSN: 2326-831X, DOI: 10.1146/annurev-statistics-031017-100101
Annual Review of Statistics and Its Application 5/1 2020-04-15

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