Books
[3] Denuit, M., Hainaut, D. and J. Trufin (2020). Effective Statistical Learning Methods for Actuaries II: Tree-based Methods and Extensions, Springer Actuarial Series.
[2] Denuit, M., Hainaut, D. and J. Trufin (2019). Effective Statistical Learning Methods for Actuaries III: Neural Networks and Extensions, Springer Actuarial Series.
[1] Denuit, M., Hainaut, D. and J. Trufin (2019). Effective Statistical Learning Methods for Actuaries I: GLMs and Extensions, Springer Actuarial Series.
Scientific Papers (International Peer-Reviewed Journals)
[46] Willame, G., Trufin, J. and M. Denuit (2024). Boosted Poisson regression trees: A guide to the BT package in R. To appear in Annals of Actuarial Science.
[45] Denuit, M. And J. Trufin (2024). Convex and Lorenz orders under balance correction in nonlife insurance pricing: review and new developments. Insurance: Mathematics and Economics 118, 123-128.
[44] Simon, P-A., Trufin, J. and M. Denuit (2024). Bivariate Poisson credibility model and bonus-malus scale for claim and near-claim events. To appear in North American Actuarial Journal.
[43] Denuit, M., Huyghe, J., Trufin, J. and T. Verdebout (2024). Testing for auto-calibration with Lorenz and Concentration curves. Insurance: Mathematics and Economics 117, 130-139.
[42] Huyghe, J., Trufin, J. and M. Denuit (2024). Boosting cost-complexity pruned trees on Tweedie responses: the ABT machine for insurance ratemaking. Scandinavian Actuarial Journal 2024(5), 417-439.
[41] Denuit, M. and J. Trufin (2023). Model selection with Pearson's correlation, concentration and Lorenz curves under autocalibration. European Actuarial Journal 13, 871-878.
[40] Sinner, C., Dominicy, Y., Trufin, J., Waterschoot, W., Weber, P. and C. Ley (2023). From Pareto to Weibull - A constructive review of distributions on R. International Statistical Review 91(1), 35-54.
[39] Ciatto, N., Verelst, H., Trufin, J. and M. Denuit (2023). Does autocalibration improve goodness of lift? European Actuarial Journal 13, 479-486.
[38] Mesfioui, M., Trufin, J. and P. Zuyderhoff (2022). Bounds on Spearman’s rho when at least one random variable is discrete. European Actuarial Journal 12, 321-348.
[37] Hainaut, D., Trufin, J. and M. Denuit (2022). Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link. Scandinavian Actuarial Journal 2022(10), 841-866.
[36] Callant, J., Trufin, J. and P. Zuyderhoff (2022). Some expressions of a generalized version of the expected time in the red and the expected area in red. Methodology and Computing in Applied Probability 24, 595-611.
[35] Mesfioui, M. and J. Trufin (2022). Best upper and lower bounds on Spearman's rho for zero-inflated continuous variables and their application to insurance. European Actuarial Journal 12, 417-423.
[34] Corradin, A., Denuit, M., Detyniecki, M., Grari, V., Sammarco, M. and J. Trufin (2022). Joint modeling of claim frequencies and behavorial signals in motor insurance. Astin Bulletin 52(1), 33-54.
[33] Mesfioui, M. and J. Trufin (2022). Bounds on multivariate Kendall's tau and Spearman's rho for zero-inflated continuous variables and their application to insurance. Methodology and Computing in Applied Probability 24, 1051-1059.
[32] Denuit, M., Charpentier, A. and J. Trufin (2021). Autocalibration and Tweedie-dominance for insurance pricing with machine learning. Insurance: Mathematics and Economics 101 (Part B), 485-497.
[31] Denuit, M., Trufin, J. and T. Verdebout (2021). Testing for more positive expectation dependence with application to model comparison. Insurance: Mathematics and Economics 101 (Part B), 163-172.
[30] Denuit, M. and J. Trufin (2021). Generalization error for Tweedie models: decomposition and error reduction with bagging. European Actuarial Journal 11(1), 325-331.
[29] Mesfioui, M. and J. Trufin (2021). Dispersive order comparisons on extreme order statistics from homogeneous dependent random vectors. Dependence Modeling 9, 385-393.
[28] Bettonville, C., d'Oultremont, L., Denuit, M., Trufin, J. and R. Van Oirbeek (2021). Matrix calculation for ultimate and one-year risk in the semi-Markov individual loss reserving model. Scandinavian Actuarial Journal 2021(5), 380-407.
[27] Gauchon, R., Loisel, S., Rullière J-L. and J. Trufin (2021). Optimal prevention of large risks with two types of claims. Scandinavian Actuarial Journal 2021(4), 323-334.
[26] Pechon, F., Denuit, M. and J. Trufin (2021). Home and Motor insurance joined at a household level using multivariate credibility. Annals of Actuarial Science 15(1), 82-114.
[25] Cossette, H., Marceau, E., Trufin, J. and P. Zuyderhoff (2020). Ruin-based risk measures in discrete-time risk models. Insurance: Mathematics and Economics, 93, 246-261.
[24] Pechon, F., Trufin, J. and M. Denuit (2020). Preliminary selection of risk factors in P&C ratemaking. Variance: Advancing the Science of Risk 13(1), 124-140.
[23] Gauchon, R., Loisel, S., Rullière J-L. and J. Trufin (2020). Optimal prevention strategies in the classical risk model. Insurance: Mathematics and Economics 91, 202-208.
[21] Pechon, F., Denuit, M. and J. Trufin (2019). Multivariate modelling of multiple guarantees in motor insurance of a household. European Actuarial Journal 9, 575-602.
[21] Denuit, M., Mesfioui, M. and J. Trufin (2019). Concordance-based predictive measures in regression models for discrete responses. Scandinavian Actuarial Journal 2019(10), 824-836.
[20] Denuit, M., Sznajder, D. and J. Trufin (2019). Model selection based on Lorenz and concentration curves, Gini indices and convex order. Insurance: Mathematics and Economics 89, 128-139.
[19] Hanbali, H., Claassens, H., Denuit, M., Dhaene, J. and J. Trufin (2019). Once covered, forever covered: The actuarial challenges of the Belgian private health insurance system. Health Policy 123(10), 970-975.
[18] Denuit, M., Guillen, M. and J. Trufin (2019). Multivariate credibility modeling for usage-based motor insurance pricing with behavioral data. Annals of Actuarial Science 13(2), 378-399.
[17] Hanbali, H., Denuit, M., Dhaene, J. and J. Trufin (2019). A dynamic equivalence principle for systematic longevity risk management. Insurance: Mathematics and Economics 86, 158-167.
[16] Denuit, M., Mesfioui, M. and J. Trufin (2019). Bounds on concordance-based validation statistics in regression models for binary responses. Methodology and Computing in Applied Probability 21(2), 491-509.
[15] Pechon, F., Trufin, J. and M. Denuit (2018). Multivariate modelling of household claim frequencies in motor third-party liability insurance. Astin Bulletin 48(3), 969-993.
[14] Denuit, M. and J. Trufin (2018). Collective loss reserving with two types of claims in motor third party liability insurance. Journal of Computational and Applied Mathematics 335, 168-184.
[13] Lefèvre, C., Trufin, J. and P. Zuyderhoff (2017). Some comparison results for finite-time ruin probabilities in the classical risk model. Insurance: Mathematics and Economics 77, 143-149.
[12] Denuit, M. and J. Trufin (2017). Beyond the Tweedie reserving model: the collective approach to loss development. North American Actuarial Journal 21(4), 611-619.
[11] Denuit, M., Dhaene, J., Hanbali, H., Lucas, N. and J. Trufin (2017). Updating mechanism for lifelong insurance contracts subject to medical inflation. European Actuarial Journal 7(1), 133-163.
[10] Mitric, I.R. and J. Trufin (2016). On a risk measure inspired from the ruin probability and the expected deficit at ruin. Scandinavian Actuarial Journal 2016, 932-951.
[9] Abdallah, A., Boucher, J-P., Cossette, H. and J. Trufin (2016). Sarmanov family of bivariate distributions for multivariate loss reserving analysis. North American Actuarial Journal 20(2), 184-200.
[8] Denuit, M. and J. Trufin (2016). From regulatory life tables to stochastic mortality projections: the exponential decline model. Insurance: Mathematics and Economics 71, 295-303.
[7] Cossette, H., Marceau, E., Larrivée-Hardy, E. and J. Trufin (2015). A note on compound renewal risk models with dependence. Journal of Computational and Applied Mathematics 285, 295-311.
[6] Denuit, M. and J. Trufin (2015). Model points and Tail-VaR in life insurance. Insurance: Mathematics and Economics 64, 268-272.
[5] Loisel, S. and J. Trufin (2014). Properties of a risk measure derived from the expected area in red. Insurance: Mathematics and Economics 55, 191-199.
[4] Loisel, S. and J. Trufin (2013). Ultimate ruin probability in discrete time with Bühlmann credibility premium adjustments. Bulletin Français d'Actuariat 13(25), 73-102.
[3] Trufin, J., Albrecher, H. and M. Denuit (2011). Properties of a risk measure derived from ruin theory. The Geneva Risk and Insurance Review 36(2), 174-188.
[2] Trufin, J., Albrecher, H. and M. Denuit (2011). Ruin problems under IBNR dynamics. Applied Stochastic Models in Business and Industry 27(2), 619-632.
[1] Trufin, J., Albrecher, H. and M. Denuit (2009). Impact of underwriting cycles on the solvency of an insurance company. North American Actuarial Journal 13(3), 385-403.
Professionally Oriented Articles (Peer-Reviewed Journals)
[5] Intervention dans le Monde de l'assurance de mai 2024, pages 21-23.
[4] Denuit, M. and J. Trufin (2020). L’excès de guaranties nuit gravement à l’assurance! L’Écho, February 25th, 2020.
[3] Denuit, M. and J. Trufin (2019). Des tables de mortalité, espérances de vie, durées de vie moyennes et probables et de leur bon usage dans l’évaluation des droits viagers. Revue du notariat belge 2019, 574-608.
[2] Denuit, M., Dhaene, J., Hanbali, H., Lucas, N. and J. Trufin (2017). Hospitalisation: le nouveau mécanisme belge d'indexation. Monde de l'assurance 2017(5), 38-46.
[1] Denuit, M. and J. Trufin (2015). Des cadences de règlement au provisionnement individuel. L'actuariel 18, 42-44.
Professionally Oriented Articles (Non-Peer-Reviewed Journals)
[14] Denuit, M. and J. Trufin (2022). How to assess broker's performances in an elementary way? FAQctuary.
[13] Bettonville, C., Denuit, M., Dubus, F. and J. Trufin (2021). Tarification d'expérience sur statistiques sinistres écrêtées. Detra Note.
[12] d'Oultremont, L., Denuit, M. and J. Trufin (2021). ICC, deviance, AUC: What are the differences? FAQctuary.
[11] Mahirwe, C., Denuit, M. and J. Trufin (2020). The reason why bagging trees outperform decision tree. Detra Note.
[10] Gagelmans, E., Denuit, M. and J. Trufin (2020). Features with flat partial dependence plots up to a certain level: not important? FAQctuary.
[9] Gagelmans, E., Denuit, M. and J. Trufin (2020). Features with flat partial dependence plots: not important? FAQctuary.
[8] Mahirwe, C., Meganck, A., Denuit, M. and J. Trufin (2020). How to combine market and internal medical indices in ORSA? FAQctuary.
[7] Denuit, M. and J. Trufin (2019). Calcul de la valeur de conversion de l'usufruit: le point de vue pragmatique de deux actuaires. Detra Note.
[6] Willame, G., Denuit, M. and J. Trufin (2019). Des triangles agrégés aux méthodes individuelles: revue de méthodes de réservation en assurances dommages. Detra Note.
[5] Denuit, M. and J. Trufin (2018). Back to the Future (2) : D’un modèle B2C intermédié à une généralisation des B2B(2C) et B2(P2P) : Enjeux actuariels. Detra Note.
[4] Denuit, M. and J. Trufin (2018). Back to the Future (1) : L’association tontinière. Detra Note.
[3] Willame, G., Denuit, M. and J. Trufin (2017). Quand Thiele et Cantelli aident l’actuaire à sortir de sa reserve. Detra Note.
[2] Gbari, S., Denuit, M. and J. Trufin (2017). Quand Vilfredo Pareto rencontre Jeanne Calment. Detra Note.
[1] Stassen, B., Denuit, M., Mahy, S., Maréchal, X. and J. Trufin (2017). A unified approach for the modelling of rating factors in workers’ compensation insurance. Reacfin White paper.
