Florian Lamalle
Florian Lamalle has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
Anne Sabourin
Université Paris Cité, MAP5 Paris France
1 shared publication- Anne Sabourin
Université Paris-Saclay
1 shared publication Stephan Clémençon
Telecom Paris, LTCI, Institut Polytechnique de Paris Paris France
1 shared publication- Stephan Clémençon
LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France
1 shared publication Vincent Feuillard
Renault Technocentre Guyancourt France
1 shared publication
Latest Publications
- 1 May 2026
- Published by Springer Nature
- p. 380-409
Predictive Learning in Survival Analysis by Empirical Maximization of Harrell’s Concordance Index
- 1 May 2026
- Published by Springer Nature
- p. 380-409
Predictive Learning in Survival Analysis by Empirical Maximization of Harrell’s Concordance Index
- 27 August 2024
- Published by Wiley in Quality and Reliability Engineering International
- Vol. 40 (8) , 4247-4261
Weibull mixture estimation based on censored data with applications to clustering in reliability engineering
It is the purpose of this paper to propose a novel clustering technique tailored to randomly censored data in reliability/survival analysis. It is based on an underlying mixture model of Weibull distributions and consists in estimating its parameters by means of a variant of the Expectation–Maximization method in the presence of random censorship. Beyond the description of the algorithm, model selection issues are addressed and we investigate its performance from an empirical perspective by applying it to possibly strongly censored (synthetic and real) survival data. The experiments carried out provide strong empirical evidence that our algorithm performs better than alternative methods standing as natural competitors in this framework.
- 27 August 2024
- Published by Wiley in Quality and Reliability Engineering International
- Vol. 40 (8) , 4247-4261
Weibull mixture estimation based on censored data with applications to clustering in reliability engineering
It is the purpose of this paper to propose a novel clustering technique tailored to randomly censored data in reliability/survival analysis. It is based on an underlying mixture model of Weibull distributions and consists in estimating its parameters by means of a variant of the Expectation–Maximization method in the presence of random censorship. Beyond the description of the algorithm, model selection issues are addressed and we investigate its performance from an empirical perspective by applying it to possibly strongly censored (synthetic and real) survival data. The experiments carried out provide strong empirical evidence that our algorithm performs better than alternative methods standing as natural competitors in this framework.