Ouvrages publiés en collaboration (3)
  1. 1. Denuit, M., Hainaut, D., & Trufin, J. (2020). Effective Statistical Learning Methods for Actuaries II: Tree-based Methods and Extensions. Switzerland: Springer Actuarial Lecture Notes.
  2. 2. Denuit, M., Hainaut, D., & Trufin, J. (2019). Effective Statistical Learning Methods for Actuaries III: Neural Networks and Extensions. Switzerland: Springer Actuarial Lecture Notes.
  3. 3. Denuit, M., Hainaut, D., & Trufin, J. (2019). Effective Statistical Learning Methods for Actuaries I: GLMs and Extension. Switzerland: Springer Actuarial Lecture Notes.
    4.   Articles dans des revues avec comité de lecture (41)
  4. 1. Simon, P.-A., Trufin, J., & Denuit, M. (2023). Bivariate Poisson credibility model and bonus-malus scale for claim and near-claim events. North American actuarial journal.
  5. 2. Gireg, W., Trufin, J., & Denuit, M. (2023). Boosted Poisson regression trees: A guide to the BT package in R. Annals of Actuarial Science.
  6. 3. Huyghe, J., Trufin, J., & Denuit, M. (2023). Boosting cost-complexity pruned trees on Tweedie responses: the ABT machine for insurance ratemaking. Scandinavian actuarial journal, 1-23. doi:10.1080/03461238.2023.2258135
  7. 4. Denuit, M., & Trufin, J. (2023). Model selection with Pearson's correlation, concentration and Lorenz curves under autocalibration. European Actuarial Journal, 13, 871-878.
  8. 5. Sinner, C., Dominicy, Y., Trufin, J., Waterschoot, W., Weber, P., & Ley, C. (2023). From Pareto to Weibull – A Constructive Review of Distributions on ℝ+. International statistical review, 91(1), 35-54. doi:10.1111/insr.12508
  9. 6. Ciatto, N., Verelst, H., Trufin, J., & Denuit, M. (2023). Does autocalibration improve goodness of lift? European Actuarial Journal, 13, 479-486.
  10. 7. Mesfioui, M., Trufin, J., & Zuyderhoff, P. (2022). Bounds on Spearman’s rho when at least one random variable is discrete. European Actuarial Journal, 12, 321-348. doi:10.1007/s13385-021-00289-8
  11. 8. Mesfioui, M., & Trufin, J. (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. doi:10.1007/s13385-021-00296-9
  12. 9. Hainaut, D., Trufin, J., & Denuit, M. (2022). Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link. Scandinavian actuarial journal, 2022(10), 841-866. doi:10.1080/03461238.2022.2037016
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