| Unravelling the predictive power of telematics data in car insurance pricing R Verbelen, K Antonio, G Claeskens Journal of the Royal Statistical Society Series C: Applied Statistics 67 (5 …, 2018 | 172 | 2018 |
| Boosting insights in insurance tariff plans with tree-based machine learning methods R Henckaerts, MP Côté, K Antonio, R Verbelen North American Actuarial Journal 25 (2), 255-285, 2021 | 89 | 2021 |
| Fitting mixtures of Erlangs to censored and truncated data using the EM algorithm R Verbelen, L Gong, K Antonio, A Badescu, S Lin ASTIN Bulletin: The Journal of the IAA 45 (3), 729-758, 2015 | 85 | 2015 |
| 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 | 81 | 2018 |
| 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 | 63 | 2017 |
| Sparse regression with multi-type regularized feature modeling S Devriendt, K Antonio, T Reynkens, R Verbelen Insurance: Mathematics and Economics 96, 248-261, 2021 | 37 | 2021 |
| 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 | 20 | 2019 |
| Multivariate mixtures of Erlangs for density estimation under censoring R Verbelen, K Antonio, G Claeskens Lifetime data analysis 22, 429-455, 2016 | 20 | 2016 |
| Multivariate mixtures of Erlangs for density estimation under censoring R Verbelen, K Antonio, G Claeskens Lifetime data analysis 22, 429-455, 2016 | 20 | 2016 |
| ReIns: Functions from “Reinsurance: Actuarial and statistical aspects” T Reynkens, R Verbelen, A Bardoutsos, D Cornilly, Y Goegebeur, ... R package version 1 (10), 2020 | 12 | 2020 |
| Phase-type distributions & mixtures of erlangs R Verbelen University of Leuven Faculty of Science Leuven Statistics Research Centre 126, 2013 | 11 | 2013 |
| An EM algorithm to model the occurrence of events subject to a reporting delay R Verbelen, K Antonio, G Claeskens, J Crevecoeur | 8 | 2018 |
| ReIns: Functions from" Reinsurance: Actuarial and Statistical Aspects", 2018 T Reynkens, R Verbelen URL https://CRAN. R-project. org/package= ReIns. R package version 1 (8), 2, 0 | 7 | |
| Modeling the occurrence of events subject to a reporting delay via an EM algorithm R Verbelen, K Antonio, G Claeskens, J Crevecoeur Statistical Science 37 (3), 394-410, 2022 | 6 | 2022 |
| 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 | 5 | 2019 |
| Predicting daily IBNR claim counts using a regression approach for the occurrence of claims and their reporting delay R Verbelen, K Antonio, G Claeskens, J Crèvecoeur Working paper, 2017 | 5 | 2017 |
| A time change strategy to model reporting delay dynamics inclaims reserving J Crèvecoeur, K Antonio, R Verbelen CFE/ERCIM, Location: Birkbeck and University of London, 2017 | 3 | 2017 |
| Multivariate mixtures of Erlangs for density estimation under censoring and truncation R Verbelen, K Antonio, G Claeskens IBioStat, Date: 2015/01/30-2015/01/30, Location: Hasselt (Belgium), 2015 | 3 | 2015 |
| 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 | 2 | 2014 |
| Unraveling the predictive power of telematics data in car insurance pricing K Antonio, G Claeskens, R Verbelen Invited seminar at Heriot Watt university, Location: Edinburgh, Scotland, 2017 | 1 | 2017 |
