A. Charpentier, E. Flachaire, E. Gallic
Optimal Transport for Counterfactual Estimation: A Method for Causal Inference
.
arXiv:2301.07755, 2022. doi:
@ARTICLE{counterfactual,
author = {Arthur Charpentier and Emmanuel Flachaire and Ewen Gallic},
title = "{Optimal Transport for Counterfactual Estimation: A Method for Causal Inference }",
journal = {ArXiv:2212.09192},
year = 2023
}
H. Hu, A. Charpentier, M. Ghossoub, A. Schied
Multiarmed Bandits Problem Under the Mean-Variance Setting
.
arXiv:2212.09192, 2022. doi:
@ARTICLE{bandits,
author = {Hongda Hu and Arthur Charpentier and Mario Ghossoub and Alexander Schied},
title = "{Multiarmed Bandits Problem Under the Mean-Variance Setting}",
journal = {ArXiv:2212.09192},
year = 2022
}
M. Hassan, N. Sakr, A. Charpentier
Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach
.
arXiv:2207.01010, 2022. doi:
@ARTICLE{RL-cat,
author = {Menna Hassan and Nourhan Sakr and Arthur Charpentier},
title = "{Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach}",
journal = {ArXiv:2207.01010},
year = 2022
}
L. Barry, A. Charpentier
L'équité de l'apprentissage machine en assurancehal-03561709, 2022. doi:
@ARTICLE{p2p,
author = {L. Barry and A. Charpentier},
title = "{L'équité de l'apprentissage machine en assurance}",
journal = {hal-03561709},
year = 2022
}
L. Barry, A. Charpentier
The Fairness of Machine Learning in Insurance: New Rags for an Old Man?
.
hal-03561709, 2022. doi:
@ARTICLE{fairLB,
author = {L. Barry and A. Charpentier},
title = "{The Fairness of Machine Learning in Insurance: New Rags for an Old Man? }",
journal = {ArXiv},
year = 2022
}
V. Grari, A. Charpentier, V. Lamprier, M. Detyniecki
A fair pricing model via adversarial learning
.
ArXiv: 2202.12008, 2022. doi:
@ARTICLE{fairVG,
author = {V. Grary and A. Charpentier and V. Lamprier and M. Detyniecki},
title = "{A fair pricing model via adversarial learning}",
journal = {ArXiv},
year = 2022,
volume = {2202.12008}
}
A. Charpentier, L. Kouakou, M. Löwe, P. Ratz, F. Vermet
Collaborative Insurance Sustainability and Network Structure.
ArXiv: 2107.02764, 2021.
@ARTICLE{p2p,
author = {A. Charpentier and L. Kouakou and M. Löwe and P. Ratz and F. Vermet},
title = "{Collaborative Insurance Sustainability and Network Structure}",
journal = {ArXiv:2107.02764},
year = 2021
}
O. Cabrignac, A. Charpentier, E. Gallic
Modeling Joint Lives within Families.
arxiv-2006.08446 2020.
@ARTICLE{covid,
author = {Olivier Cabrignac and Arthur Charpentier and Ewen Gallic},
title = "{Modeling Joint Lives within Families}",
journal = {ArXiv},
year = {2020}
}
A. Charpentier, A. Galichon, L. Vernet
Optimal transport on large networks a practitioner guide.
arXiv:1907.02320 2019.
@ARTICLE{Net,
author = {Arthur Charpentier and Alfred Galichon and Lucas Vernet},
title = "{Optimal transport on large networks a practitioner guide}",
journal = {},
year = 2019
}
A. Barry , K. Oualkacha, A. Charpentier
Weighted asymmetric least squares regression for longitudinal data using GEE.
arxiv-1810.09214 2017.
@ARTICLE{options,
author = {Amadou Barry, and Karim Oualkacha, and Arthur Charpentier},
title = "{Weighted asymmetric least squares regression for longitudinal data using GEE}",
journal = {},
year = 2018
}
A.D. Barry , A. Charpentier & K. Oualkacha
Quantile and Expectile Regression for random effects model . hal-01421752 2016.
@unpublished{diogobarry:hal-01421752,
TITLE = {{Quantile and Expectile Regression for random effects model}},
AUTHOR = {Diogo Barry, Amadou and Charpentier, Arthur and Oualkacha, Karim},
URL = {https://hal.archives-ouvertes.fr/hal-01421752},
NOTE = {working paper or preprint},
YEAR = {2016},
MONTH = Dec,
KEYWORDS = {quantiles ; Expectiles ; random effects ; longitudinal ; panel data},
PDF = {https://hal.archives-ouvertes.fr/hal-01421752/file/QuantExpect.pdf},
HAL_ID = {hal-01421752},
HAL_VERSION = {v1},
}
A.Charpentier, D.Causeur Large-scale significance testing of the full Moon effect on deliveries . Large-scale significance testing of the full Moon effect on deliveries 2009.
@unpublished{charpentier:hal-00482743,
TITLE = {{Large-scale significance testing of the full Moon effect on deliveries}},
AUTHOR = {Charpentier, Arthur and Causeur, David},
URL = {https://hal.archives-ouvertes.fr/hal-00482743},
NOTE = {working paper or preprint},
YEAR = {2009},
MONTH = Mar,
KEYWORDS = {Number of births ; Full Moon effect ; High-dimensional data ; Multiple testing},
PDF = {https://hal.archives-ouvertes.fr/hal-00482743/file/fullmoon.pdf},
HAL_ID = {hal-00482743},
HAL_VERSION = {v1},
}
A. Barry, K. Oualkacha, A. Charpentier
Alternative fixed-effects panel model using weighted asymmetric least squares regression
.
Statistical Methods & Applications, 2023. doi:
@ARTICLE{expectiles2023,
author = {A. Barry and K. Oualkacha and A. Charpentier},
title = "{Alternative fixed-effects panel model using weighted asymmetric least squares regression}",
journal = {Statistical Methods \& Applications},
year = 2023
}
L. Barry, A. Charpentier
L'équité de l'apprentissage machine en assurance Statistique et Société, 2023.
@ARTICLE{sets,
author = {L. Barry and A. Charpentier},
title = "{L'équité de l'apprentissage machine en assurance}",
journal = {hal-03561709},
year = 2022
}
A. Charpentier, M. James, H. Ali
Predicting Drought and Subsidence Risks in France
.
Natural Hazards and Earth System Sciences., 2022. doi:10.5194/nhess-2021-214
@ARTICLE{p2p,
author = {A. Charpentier and M. James and H. Ali},
title = "{Predicting Drought and Subsidence Risks in France }",
journal = { Natural Hazards and Earth System Sciences.},
year = 2022
}
A. Charpentier, Flachaire E.
Pareto models for top incomes and wealth.
Journal of Economic Inequality, 2021. doi:10.1007/s10888-021-09514-6
@ARTICLE{ineq,
author = {Charpentier, A. and Flachaire, M.},
title = "{Pareto models for top incomes and wealth}",
journal = {Journal of Economic Inequality},
year = 2021
}
A. Cayol, R. Bigot, A. Charpentier,
Risque de pandémie, pertes d’exploitation et incertitudes des garanties assurantielles.
Responsabilité civile et assurance, 2022.
@ARTICLE{RCA,
author = {Amandine Cayol, Rodolphe Bigot and Arthur Charpentier},
title = "{Risque de pandémie, pertes d’exploitation et incertitudes des garanties assurantielles}",
journal = {Responsabilité civile et assurance},
year = 2022
}
A. Charpentier, Denuit, M., Trufin, J.
Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning.
Insurance: Mathematics & Economics, 2021. doi:10.1016/j.insmatheco.2021.09.001
@ARTICLE{Autocalibration,
author = {Charpentier, A. and Denuit, M. and Trufin, J.},
title = "{Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning}",
journal = {Insurance: Mathematics \& Economics},
year = 2021
}
A. Barry, K. Oualkacha, A. Charpentier
A new GEE method to account for heteroscedasticity, using asymmetric least-square regressions.
Journal of Applied Statistics 2021. doi:10.1080/02664763.2021.1957789
@ARTICLE{CatNat,
author = {A. Barry and K. Oualkacha and A. Charpentier},
title = "{A new GEE method to account for heteroscedasticity, using asymmetric least-square regressions}",
journal = {Journal of Applied Statistics},
year = 2021
}
A. Charpentier, S. Mussard, T. Ouraga
Principal Component Analysis: A Generalized Gini Approach.
European Journal of Operational Research 2021.doi:10.1016/j.ejor.2021.02.010
@ARTICLE{CatNat,
author = {Charpentier, A. and Mussard, S. and Ouraga, T.},
title = "{Principal Component Analysis: A Generalized Gini Approach}",
journal = {European Journal of Operational Research},
year = 2021
}
A. Charpentier, R. Élie, C. Remlinger
Reinforcement Learning in Economics and Finance.
Computational Economics, 2021.doi:10.1007/s10614-021-10119-4
@ARTICLE{RL,
author = {Arthur Charpentier and Romual \'Elie and Carl Remlinger},
title = "{Reinforcement Learning in Economics and Finance}",
journal = {Computational Economics},
year = {2021}
}
A. Charpentier, L. Barry, M. James
Insurance against Natural Catastrophes: Balancing Actuarial Fairness and Social Solidarity.
Geneva Paper on Risk and Insurance, 2021.doi:10.1057/s41288-021-00233-7
@ARTICLE{CatNat,
author = {Charpentier, A. and Barry, L. and Molly, J.},
title = "{Insurance against Natural Catastrophes: Balancing Actuarial Fairness and Social Solidarity}",
journal = {Geneva Paper on Risk \& Insurance},
year = 2021
}
A. Charpentier, R. Élie, M. Laurière, V.C. Tran
COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability.
Mathematical Modelelling of Natural Phenomena 2020.doi:10.1051/mmnp/2020045
@ARTICLE{covid,
author = {Arthur Charpentier and Romual \'Elie and Mathieu Lauri\`ere and Viet Chi Tran},
title = "{COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability}",
journal = {Mathematical Modelelling of Natural Phenomena},
year = {2020}
}
L. Barry & A. Charpentier
Personalization as a Promise: Can Big Data Change the Practice of Insurance?.
Big Data & Society, 2020. doi:10.1177/2053951720935143
@ARTICLE{bigdata,
author = {Barry, L. and {Charpentier}, A.},
title = "{Personalization as a promise: Can Big Data change the practice of insurance? }",
journal = {Big Data \& Society},
url = {https://journals.sagepub.com/doi/10.1177/2053951720935143},
year = 2020
}
A. Charpentier & E. Gallic .
Can historical demography benefit from the collaborative data of genealogy websites?.
Population 2020 doi:10.3917/popu.2002.0391
@ARTICLE{ined,
author = {{Charpentier}, A. and Gallic, E.},
title = "{La démographie historique peut-elle tirer profit des données collaboratives des sites de généalogie ?}",
journal = {Population},
year = 2020
}
A. Charpentier & E. Gallic.
Using collaborative genealogy data to study migration: a research note. The History of the Family 2019,
doi:10.1080/1081602X.2019.1641130
A. Charpentier, N., Ka, S. Mussard & O.H. Ndiaye
Gini Regressions and Heteroskedasticity. Econometrics, 7, 2019,
doi:10.3390/econometrics7010004
@Article{econometrics7010004,
AUTHOR = {Charpentier, Arthur and Ka, Ndéné and Mussard, Stéphane and Ndiaye, Oumar Hamady},
TITLE = {Gini Regressions and Heteroskedasticity},
JOURNAL = {Econometrics},
VOLUME = {7},
YEAR = {2019},
NUMBER = {1},
ARTICLE-NUMBER = {4},
URL = {http://www.mdpi.com/2225-1146/7/1/4},
ISSN = {2225-1146},
ABSTRACT = {We propose an Aitken estimator for Gini regression. The suggested A-Gini estimator is proven to be a U-statistics. Monte Carlo simulations are provided to deal with heteroskedasticity and to make some comparisons between the generalized least squares and the Gini regression. A Gini-White test is proposed and shows that a better power is obtained compared with the usual White test when outlying observations contaminate the data.},
DOI = {10.3390/econometrics7010004}
}
A. Charpentier & B. Coulmont
We are not alone ! (at least, most of us). Homonymy in large scale social groups. Significance 2018,
doi:10.1111/j.1740-9713.2018.01108.x
@article {SIGN:SIGN1108,
author = {Charpentier, Arthur and Coulmont, Baptiste},
title = {We are not alone! (At least, most of us aren't)},
journal = {Significance},
volume = {15},
number = {1},
issn = {1740-9713},
url = {http://dx.doi.org/10.1111/j.1740-9713.2018.01108.x},
doi = {10.1111/j.1740-9713.2018.01108.x},
pages = {28--33},
year = {2018},
}
A. Charpentier, A. David
& R. Elie Optimal Claiming Strategies in Bonus Malus Systems and Implied Markov Chains
. Risks, 2017.
@Article{risks4020012,
AUTHOR = {Charpentier, Arthur and David, Arthur and Elie, Romuald},
TITLE = {Optimal Claiming Strategies in Bonus Malus Systems and Implied Markov Chains },
JOURNAL = {Risks},
VOLUME = {5},
YEAR = {2017},
NUMBER = {xx},
ARTICLE NUMBER = {xx},
URL = {http://www.mdpi.com/2227-9091/},
ISSN = {xxx},
ABSTRACT = {In this paper, we investigate the impact of the claim reporting strategy of drivers, within a bonus malus system. We exhibit the induced modification of the corresponding class level transition matrix and derive the optimal reporting strategy for rational drivers. The hunger for bonuses induces optimal thresholds under which, drivers do not claim their losses. A numerical algorithm is provided for computing such thresholds and realistic numerical applications are discussed.},
DOI = {10.3390/xxxxxx}
}
A. Charpentier & M. Pigeon Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)
. Risks,, 4: 1-18, 2016.
@@Article{risks4020012,
AUTHOR = {Charpentier, Arthur and Pigeon, Mathieu},
TITLE = {Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)},
JOURNAL = {Risks},
VOLUME = {4},
YEAR = {2016},
NUMBER = {2},
ARTICLE NUMBER = {12},
URL = {http://www.mdpi.com/2227-9091/4/2/12},
ISSN = {2227-9091},
ABSTRACT = {Traditionally, actuaries have used run-off triangles to estimate reserve (“macro” models, on aggregated data). However, it is possible to model payments related to individual claims. If those models provide similar estimations, we investigate uncertainty related to reserves with “macro” and “micro” models. We study theoretical properties of econometric models (Gaussian, Poisson and quasi-Poisson) on individual data, and clustered data. Finally, applications in claims reserving are considered.},
DOI = {10.3390/risks4020012}
}
G.Geenens, A.Charpentier, D.Paindaveine Probit transformation for nonparametric kernel estimation of the copula density. Bernoulli, 23: 1848-1873, 2017.
@article{geenens2017,
author = "Geenens, Gery and Charpentier, Arthur and Paindaveine, Davy",
doi = "10.3150/15-BEJ798",
fjournal = "Bernoulli",
journal = "Bernoulli",
month = "08",
number = "3",
pages = "1848--1873",
publisher = "Bernoulli Society for Mathematical Statistics and Probability",
title = "Probit transformation for nonparametric kernel estimation of the copula density",
url = "http://dx.doi.org/10.3150/15-BEJ798",
volume = "23",
year = "2017"
}
A.Charpentier, E.Flachaire Log-transform kernel density estimation
of income distribution. Actualité Economique, 91 :141-149, 2015.
@Article{RePEc:ris:actuec:0116,
author={Charpentier, Arthur and Flachaire, Emmanuel},
title={{Log-Transform Kernel Density Estimation Of Income Distribution}},
journal={L'Actualité Economique},
year=2015,
volume={91},
number={1-2},
pages={141-159},
month={Mars-Juin},
keywords={},
doi={},
abstract={Standard kernel density estimation methods are very often used in practice to estimate density functions. It works well in numerous cases. However, it is known not to work so well with skewed, multimodal and heavy-tailed distributions. Such features are usual with income distributions, defined over the positive support. In this paper, we show that a preliminary logarithmic transformation of the data, combined with standard kernel density estimation methods, can provide a much better fit of the density estimation.},
url={https://ideas.repec.org/a/ris/actuec/0116.html}
}
C. Tavéra, J.-C. Poutineau, J.-S. Pentecôte, I. Cadoret-David, A. Charpentier, C. Guéguen, M. Huchet-Bourdon, J. Licheron & G. L'Oeillet
The "Mother of All Puzzles" at thirty: a meta-analysis. International Economics, 141 :80-96, 2015.
@article{201580,
title = "The “mother of all puzzles” at thirty: A meta-analysis ",
journal = "International Economics ",
volume = "141",
number = "",
pages = "80 - 96",
year = "2015",
note = "",
issn = "2110-7017",
doi = "https://doi.org/10.1016/j.inteco.2015.01.001",
url = "http://www.sciencedirect.com/science/article/pii/S2110701715000062",
author = "Christophe Tavéra and Jean-Christophe Poutineau and Jean-Sébastien Pentecôte and Isabelle Cadoret and Arthur Charpentier and Chantal Gueguen and Maryline Huchet and Julien Licheron and Guillaume Loeillet and Nathalie Payelle and Sébastien Pommier",
keywords = "Feldstein–Horioka coefficient",
keywords = "Capital mobility",
keywords = "Saving-investment correlation",
keywords = "Meta-analysis ",
abstract = "Abstract This paper provides a meta-analysis of 1651 point estimates of Feldstein and Horioka saving retention coefficient from 49 peer-reviewed papers published over three decades. We get two main results. First, correcting for publication bias, we find a consistent underlying coefficient lying between 0.56 and 0.67 for studies using the original paper. Second, heterogeneity reported in the estimates of the Feldstein and Horioka can be explained by a few main factors. In particular, we find evidence that the saving retention coefficient is systematically underestimated with models written in first difference, models using the saving ratio or the current account ratio as the dependent variable instead of the investment ratio, and models including indicators of the public deficit or indicators of the country size as additional explanatory variables. "
}
M.T.Bastos, A.Charpentier, D.Mercea Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media. Journal of Communication, 65: 320–350, 2015.
@article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
A.Charpentier, B.Le Maux Natural catastrophe insurance: How should the government intervene?. Journal of Public Economics, 115: 1-17, 2014.
@article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
@article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
A.Charpentier, A.-L.Fougères, C.Genest and J.G.Nešlehová Multivariate Archimax copula. Journal of Multivariate Analysis, 126 :118-136, 2014.
@article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
A.Charpentier, S.Mussard Income Inequality Games . Journal of Economic Inequality, 9: 529–554, 2011.
@article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
A.Charpentier On the return period of the 2003 heat wave. Climatic Change, 109: 245–260, 2011.
@Article{Charpentier2011,
author="Charpentier, Arthur",
title="On the return period of the 2003 heat wave",
journal="Climatic Change",
year="2011",
volume="109",
number="3",
pages="245--260",
abstract="Extremal events are difficult to model since it is difficult to characterize formally those events. The 2003 heat wave in Europe was not characterized by very high temperatures, but mainly the fact that night temperature were no cool enough for a long period of time. Hence, simulation of several models (either with heavy tailed noise or long range dependence) yield different estimations for the return period of that extremal event.",
issn="1573-1480",
doi="10.1007/s10584-010-9944-0",
url="http://dx.doi.org/10.1007/s10584-010-9944-0"
}
A.Charpentier, A.Oulidi Beta kernel quantile estimators of heavy-tailed loss distributions. Statistics and Computing, 20: 35–55, 2010.
@Article{Charpentier2010,
author="Charpentier, Arthur
and Oulidi, Abder",
title="Beta kernel quantile estimators of heavy-tailed loss distributions",
journal="Statistics and Computing",
year="2010",
volume="20",
number="1",
pages="35--55",
abstract="In this paper we suggest several nonparametric quantile estimators based on Beta kernel. They are applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte Carlo based study has shown that those estimators improve the efficiency of the traditional ones, not only for light tailed distributions, but also for heavy tailed, when the probability level is close to 1. We also compare these estimators with the Extreme Value Theory Quantile applied to Danish data on large fire insurance losses.",
issn="1573-1375",
doi="10.1007/s11222-009-9114-2",
url="http://dx.doi.org/10.1007/s11222-009-9114-2"
}
A.Charpentier, A.Oulidi Estimating allocations for value-at-risk portfolio optimization. Mathematical Methods of Operations Research, 69: 395, 2009.
@Article{Charpentier2008,
author="Charpentier, Arthur
and Oulidi, Abder",
title="Estimating allocations for Value-at-Risk portfolio optimization",
journal="Mathematical Methods of Operations Research",
year="2008",
volume="69",
number="3",
pages="395",
abstract="Value-at-Risk, despite being adopted as the standard risk measure in finance, suffers severe objections from a practical point of view, due to a lack of convexity, and since it does not reward diversification (which is an essential feature in portfolio optimization). Furthermore, it is also known as having poor behavior in risk estimation (which has been justified to impose the use of parametric models, but which induces then model errors). The aim of this paper is to chose in favor or against the use of VaR but to add some more information to this discussion, especially from the estimation point of view. Here we propose a simple method not only to estimate the optimal allocation based on a Value-at-Risk minimization constraint, but also to derive---empirical---confidence intervals based on the fact that the underlying distribution is unknown, and can be estimated based on past observations.",
issn="1432-5217",
doi="10.1007/s00186-008-0244-7",
url="http://dx.doi.org/10.1007/s00186-008-0244-7"
}
A.Charpentier, J.Segers Tails of multivariate Archimedean copulas. Journal of Multivariate Analysis, 100: 1521–1537, 2009.
@article{Charpentier20091521,
title = "Tails of multivariate Archimedean copulas ",
journal = "Journal of Multivariate Analysis ",
volume = "100",
number = "7",
pages = "1521 - 1537",
year = "2009",
note = "",
issn = "0047-259X",
doi = "http://doi.org/10.1016/j.jmva.2008.12.015",
url = "http://www.sciencedirect.com/science/article/pii/S0047259X08002790",
author = "Arthur Charpentier and Johan Segers",
keywords = "Archimedean copula",
keywords = "Asymptotic independence",
keywords = "Clayton copula",
keywords = "Coefficient of tail dependence",
keywords = "Complete monotonicity",
keywords = "Domain of attraction",
keywords = "Extreme value distribution",
keywords = "Frailty model",
keywords = "Regular variation",
keywords = "Survival copula",
keywords = "Tail dependence copula ",
abstract = "A complete and user-friendly directory of tails of Archimedean copulas is presented which can be used in the selection and construction of appropriate models with desired properties. The results are synthesized in the form of a decision tree: Given the values of some readily computable characteristics of the Archimedean generator, the upper and lower tails of the copula are classified into one of three classes each, one corresponding to asymptotic dependence and the other two to asymptotic independence. For a long list of single-parameter families, the relevant tail quantities are computed so that the corresponding classes in the decision tree can easily be determined. In addition, new models with tailor-made upper and lower tails can be constructed via a number of transformation methods. The frequently occurring category of asymptotic independence turns out to conceal a surprisingly rich variety of tail dependence structures. "
}
A.Charpentier Insurability of climate risks.. The Geneva Papers on Risk and Insurance, 33: 91–109, 2008.
@Article{Charpentier2008,
author="Charpentier, Arthur",
title="Insurability of Climate Risks",
journal="The Geneva Papers on Risk and Insurance - Issues and Practice",
year="2008",
volume="33",
number="1",
pages="91--109",
abstract="The IPCC 2007 report noted that both the frequency and strength of hurricanes, floods and droughts have increased during the past few years. Thus, climate risk, and more specifically natural catastrophes, are now hardly insurable: losses can be huge (and the actuarial pure premium might even be infinite), diversification through the central limit theorem is not possible because of geographical correlation (a lot of additional capital is required), there might exist no insurance market since the price asked by insurance companies can be much higher than the price householders are willing to pay (short-term horizon of policyholders), and, due to climate change, there is more uncertainty (and thus additional risk). The first idea we will discuss in this paper, about insurance markets and climate risks, is that insurance exists only if risk can be transferred, not only to reinsurance companies but also to capital markets (through securitization or catastrophes options). The second one is that climate is changing, and therefore, not only prices and capital required should be important, but also uncertainty can be very large. It is extremely difficult to insure in a changing environment.",
issn="1468-0440",
doi="10.1057/palgrave.gpp.2510155",
url="http://dx.doi.org/10.1057/palgrave.gpp.2510155"
}
A.Charpentier, J.Segers Convergence of Archimedean copulas. Statistics and Probability Letters, 78: 412-419, 2008.
@Article{RePEc:eee:stapro:v:78:y:2008:i:4:p:412-419,
author={Charpentier, Arthur and Segers, Johan},
title={{Convergence of Archimedean copulas}},
journal={Statistics \& Probability Letters},
year=2008,
volume={78},
number={4},
pages={412-419},
month={March},
keywords={ Archimedean copula Generator Kendall distribution function},
doi={},
abstract={Convergence of a sequence of bivariate Archimedean copulas to another Archimedean copula or to the comonotone copula is shown to be equivalent with convergence of the corresponding sequence of Kendall distribution functions. No extra differentiability conditions on the generators are needed.},
url={https://ideas.repec.org/a/eee/stapro/v78y2008i4p412-419.html}
}
A.Charpentier, J.Segers Lower tail dependence for Archimedean copulas: Characterizations and pitfalls. Insurance: Mathematics and Economics, 40: 525-532, 2007.
@Article{RePEc:eee:stapro:v:78:y:2008:i:4:p:412-419,
author={Charpentier, Arthur and Segers, Johan},
title={{Convergence of Archimedean copulas}},
journal={Statistics \& Probability Letters},
year=2008,
volume={78},
number={4},
pages={412-419},
month={March},
keywords={ Archimedean copula Generator Kendall distribution function},
doi={},
abstract={Convergence of a sequence of bivariate Archimedean copulas to another Archimedean copula or to the comonotone copula is shown to be equivalent with convergence of the corresponding sequence of Kendall distribution functions. No extra differentiability conditions on the generators are needed.},
url={https://ideas.repec.org/a/eee/stapro/v78y2008i4p412-419.html}
}
A.Charpentier, A.Juri Limiting dependence structures for tail events, with applications to credit derivatives. Journal of Applied Probability, 43: 563-586, 2006.
@article{charpentier_juri_2006, title={Limiting dependence structures for tail events, with applications to credit derivatives}, volume={43}, DOI={10.1017/S0021900200001832}, number={2}, journal={Journal of Applied Probability}, publisher={Cambridge University Press}, author={Charpentier, Arthur and Juri, Alessandro}, year={2006}, pages={563–586}}
Boüette, J.-C., Chassagneux, J.-F., Sibaï, D., Terron., R. & Charpentier, A Wind in Ireland: long memory or seasonal effect?. Stochastic Environmental Research and Risk Assessment, 20: 141, 2006.
@Article{Bouette2006,
author="Bouette, Jean-Christophe
and Chassagneux, Jean-Fran{\c{c}}ois
and Sibai, David
and Terron, R{\'e}mi
and Charpentier, Arthur",
title="Wind in Ireland: long memory or seasonal effect?",
journal="Stochastic Environmental Research and Risk Assessment",
year="2006",
volume="20",
number="3",
pages="141",
abstract="Since Haslett and Raftery's paper Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource (1989), modelling meteorological time series with long memory processes, in particular the ARFIMA model has become very common. Haslett and Raftery fitted an ARFIMA model on Irish daily wind speeds. In this paper, we try to reproduce Haslett and Raftery's results (focusing on the dynamic of the wind process, and not on cross-correlation and space dependencies), and show that an ARFIMA model does not properly capture the behaviour of the series (in Modelling daily windspeed in Ireland section). Indeed, the series show a periodic behaviour, that is not taken into account by the ARFIMA model. Removing this periodic behaviour yields no results either, we therefore try to fit a GARMA model that takes into account both seasonality and long memory (in Seasonality and long memory using GARMA models section). If a GARMA process can be fitted to the data to model Irish daily data, we will show that these models could also be used to model Dutch hourly data.",
issn="1436-3259",
doi="10.1007/s00477-005-0029-y",
url="http://dx.doi.org/10.1007/s00477-005-0029-y"
}
A. Charpentier
Y a-t-il des morts acceptables ? ou comment finir une pandémie . Risques
@article{fin_pandemie,
author = "Charpentier, Arthur",
journal = "Risques",
number = "131",
title = "Y a-t-il des morts acceptables ? ou comment finir une pandémie",
year = "2022"
}
A. Charpentier
Y a-t-il des morts acceptables ? ou comment finir une pandémie. Risques
@article{morts,
author = "Charpentier, Arthur",
journal = "Risques",
number = "131",
title = "Y a-t-il des morts acceptables ? ou comment finir une pandémie",
year = "2022"
}
A. Charpentier
Le mythe de l'interprétabilité et de l'explicabilité des modèles. Risques
@article{mythe,
author = "Charpentier, Arthur",
journal = "Risques",
number = "128",
title = "Le mythe de l'interprétabilité et de l'explicabilité des modèles",
year = "2021"
}
A. Charpentier
Assurance et discrimination, quel rôle pour les actuaires ? . Risques
@article{disc,
author = "Charpentier, Arthur",
journal = "Risques",
number = "2021",
title = "Assurance et discrimination, quel rôle pour les actuaires ?",
year = "2021"
}
A. Charpentier & E. Gallic
Intelligence collective et données. Risques
@article{intel,
author = "Charpentier, Arthur and Gallic, Ewen",
journal = "Risques",
number = "2021",
title = "Intelligence collective et données.",
year = "2021"
}
A. Charpentier
Une mesure ne peut être un objectif. Risques
@article{goodhart,
author = "Charpentier, Arthur",
journal = "Risques",
number = "2021",
title = "IUne mesure ne peut être un objectif",
year = "2021"
}
A. Charpentier, L. Barry & E. Gallic
Quel avenir pour les probabilités prédictives
en assurance ?. Annales des Mines
@article{charpentierAM1,
author = "Charpentier, Arthur and Barry, Laurence and Gallic, Ewen",
fjournal = "Annales des Mines",
journal = "Annales des Mines",
number = "2020 »,
title = "Quel avenir pour les probabilités prédictives en assurance ?",
year = "2020"
}
A. Charpentier
Quel Big Data, GAFA et assurance. Annales des Mines
@article{charpentierAM2,
author = "Charpentier, Arthur",
fjournal = "Annales des Mines",
journal = "Annales des Mines",
number = "2020 »,
title = "Big Data, GAFA et assurance",
year = "2020"
}
R. Bigot & A. Charpentier
Repenser la responsabilité, et la causalité. Risques, 120.
@article{charpentier120,
author = "Bigot, Rodolphe and Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "120 »,
title = "Repenser la responsabilit\'e, et la causalit\'e",
year = "2019"
}
A. Charpentier
Les autorités publiques face aux risques, de la confiance au doute . Risques, 119.
@article{charpentier119,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "119 »,
title = "Les autorit\'es publiques face aux risques, de la confiance au doute",
year = "2019"
}
A.Charpentier, B. Cherrier
La valeur de la vie. Risques, 118.
@article{charpentier118,
author = "Charpentier, Arthur and Cherrier, B\’eatrice",
fjournal = "Risques",
journal = "Risques",
number = "118 »,
title = "La valeur de la vie",
year = "2019"
}
A.Charpentier
Les classes de risques vont-elles plus loin que les stéréotypes?. L’actuariel, 32, Mars 2019.
@article{charpentierACT,
author = "Charpentier, Arthur",
journal = "L'actuariel",
number = "32",
title = "Les classes de risques vont-elles plus loin que les stéréotypes?",
year = "2019"
}
A.Charpentier
Du pari au "marché prédictif". Variance.eu, 2019.
@article{charpentierV19,
author = "Charpentier, Arthur",
fjournal = "Variance.eu",
journal = "Variance.eu",
number = "",
title = "Du pari au marché prédictif",
year = "2019"
}
A.Charpentier
Petite histoire des paris sportifs. Variance.eu, 2019.
@article{charpentier112,
author = "Charpentier, Arthur",
fjournal = "Variance.eu",
journal = "Variance.eu",
number = "",
title = "Petite histoire des paris sportifs",
year = "2019"
}
A.Charpentier
Histoire du hasard et de la simulation. Risques, 116.
@article{charpentier112,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "116",
title = "Histoire du hasard et de la simulation",
year = "2018"
}
A.Charpentier
La représentation cartographique des villes. Variance.eu, 2018.
@article{charpentiervilles,
author = "Charpentier, Arthur",
fjournal = "Variance.eu",
journal = "Variance.eu",
number = "",
title = "La représentation cartographique des villes",
year = "2018"
}
A.Charpentier
news, post-truth, Wikipedia et blockchain : vérité et consensus. Risques, 115.
@article{charpentier112,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "115",
title = "Fake news, post-truth, Wikipedia et blockchain : vérité et consensus",
year = "2018"
}
A.Charpentier L'éthique de la modélisation dans un monde où la normalité n'existe plus. Risques, 112.
@article{charpentier112,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "112",
title = "L'éthique de la modélisation dans un monde où la normalité n'existe plus",
year = "2017"
}
Antonio, K. & A.Charpentier La tarification par genre en assurance, corrélation ou causalité ?. Risques, 110.
@article{charpentier108,
author = "Antonio, Katrien and Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "110",
title = "La tarification par genre en assurance, corrélation ou causalité ?",
year = "2017"
}
A.Charpentier, R.Suire Données et santé: valeurs, acteurs et santé. Risques, 107.
@article{charpentier107,
author = "Charpentier, Arthur and Suire, Raphael",
fjournal = "Risques",
journal = "Risques",
number = "107",
title = "Données et santé: valeurs, acteurs et santé",
year = "2016"
}
A.Charpentier Fibonacci, les lapins, le nombre d’or et les calculs actuariels. Risques, 106.
@article{charpentier106,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "106",
title = "Fibonacci, les lapins, le nombre d’or et les calculs actuariels",
year = "2016"
}
A.Charpentier La guerre des étoiles: distinguer le signal et le bruit. Risques, 105.
@article{charpentier105,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "105",
title = "La guerre des étoiles: distinguer le signal et le bruit",
year = "2016"
}
Charpentier, A., Eyraut-Loisel, A., Hannart, A. & Tomas, J. Changement Climatique et Assurance. Variances, 54.
@article{charpentier105,
author = "Charpentier, Arthur and Eyraut-Loisel, Anne and Hannart, Alexis and Tomas, Julien",
fjournal = "Variances",
journal = "Variances",
number = "54",
title = "Changement Climatique et Assurance",
year = "2015"
}
A.Charpentier, B.Cherrier ‘Mathiness’ et Assurance. Risques, 104.
@article{charpentier104,
author = "Charpentier, Arthur and Cherrier, B\'eatrice",
fjournal = "Risques",
journal = "Risques",
number = "104",
title = "‘Mathiness’ et Assurance",
year = "2015"
}
A.Charpentier, M.Denuit, R.Elie Segmentation et Mutualisation, les deux faces d’une même pièce. Risques, 103.
@article{charpentier103,
author = "Charpentier, Arthur and Denuit, Michel and \'Elie, Romuald",
fjournal = "Risques",
journal = "Risques",
number = "103",
title = "Segmentation et Mutualisation, les deux faces d’une m\^eme pi\`ece",
year = "2014"
}
A.Charpentier, A.Diogo Barry Big data : passer d’une analyse de corrélation à une interprétation causale. Risques, 101.
@article{charpentier104,
author = "Charpentier, Arthur and Diogo Barry, Amadou",
fjournal = "Risques",
journal = "Risques",
number = "101",
title = "Big data : passer d’une analyse de corrélation à une interprétation causale",
year = "2014"
}
A.Charpentier Interprétation, intuition et probabilités. Risques, 99.
@article{charpentier99,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "99",
title = "Interprétation, intuition et probabilités",
year = "2014"
}
A.Charpentier De la difficulté de faire des prévisions (quand on a peu de données). Risques, 98.
@article{charpentier99,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "98",
title = "De la difficulté de faire des prévisions (quand on a peu de données)",
year = "2014"
}
B.Coulmont, A.Charpentier, J.Gombin Un homme, deux voix : le vote par procuration. La Vie des Idées, 11 février 2014.
@article{charpentier99,
author = "Coulmont, Baptiste, Charpentier, Arthur and Gombin, Jo\"el",
fjournal = "La Vie des Idées",
journal = "La Vie des Idées",
title = "Un homme, deux voix : le vote par procuration",
year = "2014"
}
A.Charpentier La loi des petits nombres. Risques, 97.
@article{charpentier99,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "98",
title = "La loi des petits nombres",
year = "2013"
}
A.Charpentier L’efficience des marchés : hypothèse de modèle ou fait stylisé?. Risques, 96.
@article{charpentier99,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "96",
title = "L’efficience des marchés : hypothèse de modèle ou fait stylisé?",
year = "2013"
}
A.Charpentier La loi des grands nombres et le théorème central limite comme base de l’assurabilité ?. Risques, 86.
@article{charpentier99,
author = "Charpentier, Arthur",
fjournal = "Risques",
journal = "Risques",
number = "86",
title = "La loi des grands nombres et le théorème central limite comme base de l’assurabilité ?",
year = "2010"
}
Books
G.Bénéplanc, A.Charpentier, P.Thourot Manuel d'Assurance. Presses Universitaires de France. 2022
@Book{MANDASS,
author={Gilles B\'en\'eplance and Arthur Charpentier and Patrick Thourot},
title={Manuel d'Assurance},
year={2022},
isbn={9782130832935},
publisher={Presses Universitaires de France}
}
A.Charpentier, E.Flachaire Pareto Models for Risk Management. in
Recent Econometric Techniques for Macroeconomic and Financial Data, G. Dufrénot & T. Matsuki Ed., 2020.
@incollection{Pareto,
author = "Arthur Charpentier and Emmanuel Flachaire",
title = "Pareto Models for Risk Management",
editor = "Gilles Dufr\'enot and Takashi Matsuki",
booktitle = "Recent Econometric Techniques for Macroeconomic and Financial Data",
publisher = "Springer Verlag",
year = 2020,
page = xxx-xxxx
}
A.Charpentier, M.Denuit On limits for machine learning algorithms in insurance. in
Insurance data analytics, F.Planchet & C.Y.Robert Ed., 2020.
@incollection{Pareto,
author = "Arthur Charpentier and Michel Denuit",
title = "On limits for machine learning algorithms in insurance",
editor = "F. Planchet and C. Robert",
booktitle = "Insurance data analytics",
publisher = "Economica",
year = 2020,
page = xxx-xxxx
}
A.Charpentier Central Limit Theorem. in The SAGE Encyclopedia of Educational Research,
Measurement, and Evaluation, B. Frey Ed., 2018.
@incollection{CLT,
author = "Arthur Charpentier",
title = "Central Limit Theorem",
editor = "Bruce Frey ",
booktitle = "The SAGE encyclopedia of educational research, measurement, and evaluation",
publisher = "SAGE",
year = 2018,
page = 255-258
}
A.Charpentier Prévision avec des copules en finance. in Prévisions en Finance, Charles, Darne & Ferrara Eds., 2017.
@incollection{CAS1,
author = "Arthur Charpentier",
title = "Pr\'evision avec des copules en finance",
editor = "Am\'elie Charles, Olivier Darne and Laurent Ferrara",
booktitle = "Pr\'evision en Finance",
publisher = "Economica",
year = 2017,
chapter = 7
}
A.Charpentier, R.Kaas Introduction. in Computational Actuarial Science With R, Charpentier, A. Eds., 2014.
@incollection{CAS1,
author = "Arthur Charpentier and Rob Kaas",
title = "Introduction",
editor = "Charpentier, Arthur",
booktitle = "Computational Actuarial Science, With R",
publisher = "CRC Press",
year = 2014,
chapter = 1
}
A.Charpentier, S.Tufféry Statistical Learning. in Computational Actuarial Science With R, Charpentier, A. Eds., 2014.
@incollection{CAS1,
author = "Arthur Charpentier and St\'ephane Tuff\'ery",
title = "Statistical Learning",
editor = "Charpentier, Arthur",
booktitle = "Computational Actuarial Science, With R",
publisher = "CRC Press",
year = 2014,
chapter = 6
}
B.Escoto, A.Charpentier Bayesian Philosophy. in Computational Actuarial Science With R, Charpentier, A. Eds., 2014.
@incollection{CAS1,
author = "Ben Escoto and Arthur Charpentier",
title = "Bayesian Philosophy",
editor = "Charpentier, Arthur",
booktitle = "Computational Actuarial Science, With R",
publisher = "CRC Press",
year = 2014,
chapter = 5
}
A.Charpentier Modèles statistiques du risque en assurance. in Statistique du Risque, Droesbeke, Maumy-Bertrand, Saporta & Thomas-Agnan Eds., 2014.
@incollection{cop-charpentier,
author = "Arthur Charpentier",
title = "Modèles statistiques du risque en assurance",
editor = "Droesbeke, Jean-Jacques and Maumy-Bertrand, Myriam and Saporta, Gilbert and Thomas-Agnan , Christine",
booktitle = "Statistique du risque",
publisher = "Technip",
year = 2014,
pages = "266-290",
chapter = 3
}
A.Charpentier Copules et Risques Multiples. in Statistique du Risque, Droesbeke, Maumy-Bertrand, Saporta & Thomas-Agnan Eds., 2014.
@incollection{cop-charpentier,
author = "Arthur Charpentier",
title = "Copules et Risques Multiples",
editor = "Droesbeke, Jean-Jacques and Maumy-Bertrand, Myriam and Saporta, Gilbert and Thomas-Agnan , Christine",
booktitle = "Statistique du risque",
publisher = "Technip",
year = 2014,
pages = "266-290",
chapter = 6
}
A.Charpentier Mesures de Risques. in Statistique du Risque, Droesbeke, Maumy-Bertrand, Saporta & Thomas-Agnan Eds., 2014.
@incollection{cop-charpentier,
author = "Arthur Charpentier",
title = "Mesures de Risques",
editor = "Droesbeke, Jean-Jacques and Maumy-Bertrand, Myriam and Saporta, Gilbert and Thomas-Agnan , Christine",
booktitle = "Statistique du risque",
publisher = "Technip",
year = 2014,
pages = "266-290",
chapter = 3
}
A.Charpentier, J.D.Fermanian, O.Scaillet The estimation of copulas:
Theory and practice. in Copula Methods in Derivatives and Risk Management: From Credit Risk to Market Risk, Rank Eds., 2006.
@incollection{CSF,
author = "Arthur Charpentier and Jean-David Fermanian and Olivier Scaillet",
title = "The estimation of copulas: Theory and practice",
editor = "John Rank",
booktitle = "Copula Methods in Derivatives and Risk Management: From Credit Risk to Market Risk",
publisher = "Risk Books",
year = 2006,
chapter = 6
}
A.Charpentier Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy. Risques, 108, .
@article{Cathy,
author={Arthur Charpentier},
title={Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy},
year={2016}
}
Samuel Stocksieker
(supervised with Denys Pommeret
)
Hongda Hu
(supervised with Alexander Schied
and Mario Ghossoub
)
Philipp Ratz
PhD (former)
Enora Belz
Antoine Ly , SCOR Data Lab
(supervised with Romuald Elie
)
Diogo Barry, Amadou
, McGill
(supervised with Karim Oualkacha
)
Graduate
Suzie Grondin
Nathan Herzhaft
Kim Anh Lê
Gaspard Ichas
Olivier Côté
Graduate (former)
Franklin Feukam Kouhoue
Martin de Closets
Rawanda Matar
Lariosse Kouakou
Apollinaire Barme
Elie Odin
Molly James
Enora Belz
Clothilde Davesne
Julie Viard
Ewen Gallic
Clément Pravin
François Portier
Emmanuel Dupuy
Jeremy Payen
Clément Pravin
Benjamin Favetto
Angelo Caria
Kevin Masset
Thomas Anglade
Arnaud At
Gilles Chau
An Dinh
Aurélien Fortin
Marc Lenoir
Jérôme Ternat
Emilie Quémat
Khalil Antoine al Dayri
Jean-Christophe Bouëtté
Jean-François Chassagneux
David Sibaï
Undergraduate
-
Undergraduate (former)
Francis Proulx
Arthur David
Charles Tremblay-Bergeron
Marilou Durand
PhD Committees
HDR Jury
Christophe Dutang 2021 Some statistical and game-theoretic models with an actuarial perspective
Université Paris Daupine
Nabil Kazi-Tani 2021 Contrôle stochastique, mesures de risque et théorie de la ruine :
quelques problèmes de gestion des risques
Université de Lyon 1
PhD Jury
Mohamed Ouhourane 2023 La régression asymétrique quantile et expectile en grande dimension et la sélection de variables par groupes
UQAM, Montréal, Canada
Pierre Chatelain 2023 Tarification à l’adresse en assurance habitation individuelle
Université de Lyon, France
Guillaume Boglioni Beaulieu 2022 Pairwise versus mutual independence: visualisation, actuarial applications and central limit theorems
UNSW, Australia
Meryem Yankol Schalck 2022 Investigating New Machine Learning Approaches for
Financial Fraud Detection and Survival Analysis in
Insurance Industries
Université d’Orléans, France
Antoine Heranval 2022 Application de méthodes d’apprentissage statistique pour
l’évaluation de la nature et du coût des dommages assurés liés aux
événements naturels en France
Sorbonne Université, France
Vincent Grari 2022 Adversarial mitigation to reduce unwanted biases
in machine learning
Sorbonne Université, France
François Hu 2022 Semi-supervised learning in insurance:
fairness and active learning
Institut Polytechnique de Paris, France
Ihsan Chaoubi 2022 Modélisation de la dépendance et apprentissage automatique
Université Laval, Québec, Canada
Enora Belz 2021 Econométrie des données imparfaites : méthodes et applications
Université de Rennes 1
Arthur Maillart 2021 Quelques méthodes d’explicabilité pour les modèles d’apprentissage statistique en actuariat.
Université de Lyon 1
Debora Zaparova 2021 Information et assurance : la segmentation des risques et la prévention dans un contexte de disponibilité des données
Université de Strasbourg
Loann Desboulets 2020 Variable selection on non-linear manifolds
Aix-Marseille School of Economics
Lenin Arango Castillo 2020
Queen's University
Sander Devriendt 2020
KU Leuven
Pierrick Piette 2019 Contributions de l’Apprentissage Statistique à l’Actuariat et la Gestion des Risques Financiers
Université de Lyon 1
Oscar Alberto Quijano Xacur 2019 Computational Bayesian Methods for Insurance Premium Estimation
Concordia University, Montréal
Yang Jiao 2018 Applications of Artificial Intelligence in E-Commerce and Finance
Telecom SudParis
Edouard Debonneuil, 2018
Analyses prospectives de mortalité : approches actuarielle et biomédicale
Université de Lyon 1, France
Alexandre Godzinski, 2017 Three empirical essays on moral hazard
identification in insurance
École des Hautes Études en Sciences Sociales, PSE
Fattouma Souissi, 2016 Gini-PLS regression: an application on European agriculture incomes inequalities
Université de Montpellier
Arnaud Goussebaïle, 2015 Prevention and Insurance of Natural Catastrophes
Ecole Polytechnique
Leo Guelman, 2015 Optimal personalized treatment learning models
with insurance applications
Universitat de Barcelona
Przemyslaw Sloma, 2014 Contribution to the weak convergence
of empirical copula processes & to the stochastic claims reserving in general insurance
Université Paris VI
Mathieu Pigeon, 2014 Multivariate Skew Models with Applications in Loss
Reserving and Reinsurance
Université Catholique de Louvain
Julien Tomas, 2013 Quantifying biometric life insurance risks with nonparametric
smoothing methods
Universiteit van Amsterdam
Aymric Kamega, 2011 Outils théoriques et opérationnels
adaptés au contexte de l'assurance vie en Afrique subsaharienne francophone: Analyse et
mesure des risques liés à la mortalité
Université Lyon I
Tarek Zari, 2010 Contribution à l'étude du processus empirique de
copule
Université Paris VI
Meriem Maatig, 2010 Effets des comportements risqués
des conducteurs sur la sinistralité: Analyse empirique sur des données françaises
Université Paris II Assas
Noureddine Ben Lagha, 2008 Nouvelles approches de l'étude de
la sinistralité en assurance automobile
Université Paris II Assas
Arthur Maillart
Debora Zaparova
Samuel Piveteau
Bertille Picard
Xavier Vamparys
MSc Committees
Francis Duval
Roxane Turcotte
Olivier Binette
Julie Bélanger
Alexandre Roy-Gaumond
Reviewer
Journals
Stochastic Environmental Research and Risk Assessment
Theory and Decision
Insurance: Mathematics and Economics
Journal of Banking and Finance
The Canadian Journal of Statistics
Journal of Computational and Graphical Statistics
Journal of Multivariate Analysis
Communications in Statistics: Theory and Methods
Quantitative Finance
Journal of the American Statistical Association
TEST
Asia-Pacific Journal of Financial Studies
Statistics and Decision
Kybernetika
European Journal of Finance
Mathematics and Financial Economics
Statistica Sinica Extremes
Physics and Chemistry of the Earth
Computational Statistics
Geneva Papers on Risk and Insurance
Bernoulli Water Ressources
Statistics & Probability Letters
Mathematical Finance
Journal of Risk
Journal of Hydrology
Scandinavian Actuarial Journal
Advances in Statistical Analysis
European Actuarial Journal
Metrika
Journal of Statistical Planning and Inference
Annals of Applied Statistics
Constructive Approximation
Econometric Reviews
Annals of Economics and Statistics
Annals of Actuarial Science
Journal of the Royal Statistical Society – Series B
Mathematics of Social Sciences
Economic Theory
ASTIN Bulletin
Journal of Statistical Software
Journal of Population Economics
Risks
Journal of Zhejiang University - Science A
Journal of Time Series Analysis
European Journal of Operation Research
Econometrica
Dependence Modeling
Journal of Economic Behavior & Organization
Annals of Actuarial Science E
ntropy
Sustainability
Risk Analysis
Journal of Statistical Computation and Simulation
North American Actuarial Journal
IEEE Transactions on Information Theory
Remote Sensing
Stochastic Processes & Applications
Journal of Mathematical Economics
Journal of Risk and Insurance
PLOS One
Journal of Theoretical Biology
Bulletin de l'Association Mathématique du Québec
International Journal of Mathematics in Operational Research
Systems and Control Letters Annals of Operations Research
Books
MIT Press Springer Verlag
CRC Press
Grants & Scolarships
ANR (Agence Nationale pour la Recherche, France)
AXA Research Fund
FNR Luxembourg CORE program (Luxembourg)
FRQNT (Fonds de Recherche du Québec - Nature & Technologie, Canada)
FRS-FNRS (Fonds de la Recherche Scientifique, Belgique)
MICTACS (Mathematics of Information Technology and Complex systems, Canada)
NSA (National Security Agency - Mathematical Sciences Grant Program - U.S.A.)
NSERC-CRSNG (Natural Sciences and Engineering Research Council, Canada)
IVADO (Institut de valorisation des données, Canada)
ANRT (Agence Nationale Recherche & Technologie, France)
ISF (הקרן הלאומית למדע, Israel)
SNSF-FNS (Swiss National Science Foundation)
NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek - Dutch Research Council)
Research Grants Council (RGC) of Hong Kong
arthur charpentier
Faculté des Sciences (PK) Université du Québec à Montréal
201, avenue du Président-Kennedy, Montréal (Québec), Canada H2X 3Y7