Roel Verbelen
Roel Verbelen
Verified email at kuleuven.be - Homepage
Title
Cited by
Cited by
Year
Unraveling the predictive power of telematics data in car insurance pricing
V Roel, K Antonio, G Claeskens
Available at SSRN 2872112, 2017
602017
Fitting mixtures of Erlangs to censored and truncated data using the EM algorithm
V Roel, G Lan, K Antonio, A Badescu, XS Lin
Astin Bulletin 45 (3), 729-758, 2015
442015
Fitting mixtures of Erlangs to censored and truncated data using the EM algorithm
V Roel, G Lan, K Antonio, A Badescu, XS Lin
Astin Bulletin 45 (3), 729-758, 2015
442015
A data driven binning strategy for the construction of insurance tariff classes
R Henckaerts, K Antonio, M Clijsters, R Verbelen
Scandinavian Actuarial Journal 2018 (8), 681-705, 2018
232018
Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions
T Reynkens, R Verbelen, J Beirlant, K Antonio
Insurance: Mathematics and Economics 77, 65-77, 2017
232017
Multivariate mixtures of Erlangs for density estimation under censoring
R Verbelen, K Antonio, G Claeskens
Lifetime data analysis 22 (3), 429-455, 2016
172016
Multivariate mixtures of Erlangs for density estimation under censoring
R Verbelen, K Antonio, G Claeskens
Lifetime data analysis 22 (3), 429-455, 2016
172016
Modeling the number of hidden events subject to observation delay
J Crevecoeur, K Antonio, R Verbelen
European Journal of Operational Research 277 (3), 930-944, 2019
72019
Boosting insights in insurance tariff plans with tree-based machine learning
R Henckaerts, K Antonio, MP Côté, R Verbelen
Perspectives on Actuarial Risks in Talks of Young researchers, Location …, 2019
32019
Phase-type distributions & mixtures of Erlangs
R Verbelen
UNIVERSITY OF LEUVEN, 2013
32013
Boosting insights in insurance tariff plans with tree-based machine learning methods
R Henckaerts, MP Côté, K Antonio, R Verbelen
North American Actuarial Journal, 1-31, 2020
22020
Sparse regression with multi-type regularized feature modeling
S Devriendt, K Antonio, T Reynkens, R Verbelen
arXiv preprint arXiv:1810.03136, 2018
22018
An EM algorithm to model the occurrence of events subject to a reporting delay
R Verbelen, K Antonio, G Claeskens, J Crevecoeur
22018
Multivariate mixtures of erlangs for density estimation under censoring and truncation
R Verbelen, K Antonio, G Claeskens
KU Leuven, Fac. of Economics and Business, 2014
22014
A time change strategy to model reporting delay dynamics in claims reserving
J Crèvecoeur, K Antonio, R Verbelen
arXiv preprint arXiv:1801.02935, 2018
12018
Unravelling the predictive power of telematics data in car insurance
R Verbelen, K Antonio, G Claeskens
Ageas pricing seminar, Date: 2016/09/30-2016/09/30, Location: Brussels, Belgium, 2016
12016
Fitting mixtures of Erlangs to censored and truncated data using the EM algorithm
K Antonio, A Badescu, L Gong, S Lin, R Verbelen
KU Leuven, Fac. of Business and Economics, 2014
12014
Modeling the occurrence of events subject to a reporting delay via an EM algorithm
R Verbelen, K Antonio, G Claeskens, J Crevecoeur
arXiv preprint arXiv:1909.08336, 2019
2019
Boosting insurance tariff plans with insights from tree-based machine learning methods
R Henckaerts, K Antonio, MP Côté, R Verbelen
KU Leuven & Cass Business School PhD colloquium, Location: Leuven, Belgium, 2019
2019
Tree-based machine learning for insurance pricing
K Antonio, MP Côté, R Henckaerts, R Verbelen
Ageas pricing seminar, Location: Malaga, Spain, 2019
2019
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