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Katrien Antonio

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Jonas Crevecoeur & Katrien Antonio & Stijn Desmedt & Alexandre Masquelein, 2022. "Bridging the gap between pricing and reserving with an occurrence and development model for non-life insurance claims," Papers 2203.07145, arXiv.org, revised Feb 2023.

    Cited by:

    1. Shengkun Xie & Rebecca Luo, 2022. "Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions," Mathematics, MDPI, vol. 10(10), pages 1-19, May.

  2. Jens Robben & Katrien Antonio & Sander Devriendt, 2021. "Assessing the impact of the COVID-19 shock on a stochastic multi-population mortality model," Papers 2111.10164, arXiv.org.

    Cited by:

    1. Maria Francesca Carfora & Albina Orlando, 2023. "A Preliminary Investigation of a Single Shock Impact on Italian Mortality Rates Using STMF Data: A Case Study of COVID-19," Data, MDPI, vol. 8(6), pages 1-12, June.

  3. Jonas Crevecoeur & Jens Robben & Katrien Antonio, 2019. "A hierarchical reserving model for reported non-life insurance claims," Papers 1910.12692, arXiv.org, revised Nov 2021.

    Cited by:

    1. Sebastian Calcetero-Vanegas & Andrei L. Badescu & X. Sheldon Lin, 2023. "Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method," Papers 2307.10808, arXiv.org, revised Jul 2023.
    2. Kristian Buchardt & Christian Furrer & Oliver Lunding Sandqvist, 2022. "Transaction time models in multi-state life insurance," Papers 2209.06902, arXiv.org, revised Feb 2023.
    3. Emmanuel Jordy Menvouta & Jolien Ponnet & Robin Van Oirbeek & Tim Verdonck, 2022. "mCube: Multinomial Micro-level reserving Model," Papers 2212.00101, arXiv.org.
    4. Sebastian Calcetero-Vanegas & Andrei L. Badescu & X. Sheldon Lin, 2022. "Effective a Posteriori Ratemaking with Large Insurance Portfolios via Surrogate Modeling," Papers 2211.06568, arXiv.org, revised May 2023.
    5. Oliver Lunding Sandqvist, 2023. "A multistate approach to disability insurance reserving with information delays," Papers 2312.14324, arXiv.org.

  4. Jonas Crevecoeur & Katrien Antonio & Roel Verbelen, 2018. "Modeling the number of hidden events subject to observation delay," Papers 1801.02935, arXiv.org, revised Mar 2019.

    Cited by:

    1. Stephan M. Bischofberger, 2020. "In-Sample Hazard Forecasting Based on Survival Models with Operational Time," Risks, MDPI, vol. 8(1), pages 1-17, January.
    2. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    3. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Xian, Alan, 2021. "Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework," European Journal of Operational Research, Elsevier, vol. 290(1), pages 177-195.
    4. Benjamin Avanzi & Greg Taylor & Bernard Wong & Alan Xian, 2020. "Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework," Papers 2003.13888, arXiv.org, revised May 2020.
    5. Chehade, Abdallah & Savargaonkar, Mayuresh & Krivtsov, Vasiliy, 2022. "Conditional Gaussian mixture model for warranty claims forecasting," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    6. Oliver Lunding Sandqvist, 2023. "A multistate approach to disability insurance reserving with information delays," Papers 2312.14324, arXiv.org.

  5. Dhaene, Jan & Godecharle, Els & Antonio, Katrien & Denuit, Michel & Hanbali, Hamza, 2017. "Lifelong health insurance covers with surrender values: updating mechanisms in the presence of medical inflation," LIDAM Reprints ISBA 2017036, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Hanbali, Hamza & Claassens, Hubert & Denuit, Michel & Dhaene, Jan & Trufin, Julien, 2019. "Once covered, forever covered: The actuarial challenges of the Belgian private health insurance system," LIDAM Reprints ISBA 2019044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Hanbali, Hamza & Denuit, Michel & Dhaene, Jan & Trufin, Julien, 2019. "A dynamic equivalence principle for systematic longevity risk management," LIDAM Reprints ISBA 2019009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  6. Roel Henckaerts & Katrien Antonio & Maxime Clijsters & Roel Verbelen, 2017. "A data driven binning strategy for the construction of insurance tariff classes," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 583471, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.

    Cited by:

    1. Freek Holvoet & Katrien Antonio & Roel Henckaerts, 2023. "Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff," Papers 2310.12671, arXiv.org, revised Oct 2023.
    2. Sarra Ghaddab & Manel Kacem & Christian Peretti & Lotfi Belkacem, 2023. "Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty," Empirical Economics, Springer, vol. 65(3), pages 1105-1127, September.

  7. Tom Reynkens & Roel Verbelen & Jan Beirlant & Katrien Antonio, 2016. "Modeling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 549545, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.

    Cited by:

    1. Daniela Castro‐Camilo & Raphaël Huser & Håvard Rue, 2022. "Practical strategies for generalized extreme value‐based regression models for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 33(6), September.
    2. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2023. "Empirical risk assessment of maintenance costs under full-service contracts," European Journal of Operational Research, Elsevier, vol. 304(2), pages 476-493.
    3. Sarra Ghaddab & Manel Kacem & Christian Peretti & Lotfi Belkacem, 2023. "Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty," Empirical Economics, Springer, vol. 65(3), pages 1105-1127, September.
    4. Lambert, Philippe, 2023. "Nonparametric density estimation and risk quantification from tabulated sample moments," Insurance: Mathematics and Economics, Elsevier, vol. 108(C), pages 177-189.
    5. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation In Extreme Value Regression Models Of Hedge Fund Tail Risks," Working Papers hal-04090916, HAL.
    6. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation in Extreme Value Regression Models of Hedge Fund Tail Risks," Papers 2304.06950, arXiv.org.
    7. Emmanuel Jordy Menvouta & Jolien Ponnet & Robin Van Oirbeek & Tim Verdonck, 2022. "mCube: Multinomial Micro-level reserving Model," Papers 2212.00101, arXiv.org.
    8. Laudagé, Christian & Desmettre, Sascha & Wenzel, Jörg, 2019. "Severity modeling of extreme insurance claims for tariffication," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 77-92.
    9. Blostein, Martin & Miljkovic, Tatjana, 2019. "On modeling left-truncated loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 35-46.
    10. Bhati, Deepesh & Ravi, Sreenivasan, 2018. "On generalized log-Moyal distribution: A new heavy tailed size distribution," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 247-259.

  8. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2016. "Unraveling the predictive power of telematics data in car insurance pricing," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 552745, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.

    Cited by:

    1. Shengkun Xie, 2021. "Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation," Risks, MDPI, vol. 9(7), pages 1-21, July.
    2. Marjan Qazvini, 2019. "On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study," Risks, MDPI, vol. 7(3), pages 1-17, June.
    3. Shengkun Xie, 2019. "Defining Geographical Rating Territories in Auto Insurance Regulation by Spatially Constrained Clustering," Risks, MDPI, vol. 7(2), pages 1-20, April.
    4. Christopher Blier-Wong & Hélène Cossette & Luc Lamontagne & Etienne Marceau, 2020. "Machine Learning in P&C Insurance: A Review for Pricing and Reserving," Risks, MDPI, vol. 9(1), pages 1-26, December.
    5. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2023. "Empirical risk assessment of maintenance costs under full-service contracts," European Journal of Operational Research, Elsevier, vol. 304(2), pages 476-493.
    6. Etye Steinberg, 2022. "Run for Your Life: The Ethics of Behavioral Tracking in Insurance," Journal of Business Ethics, Springer, vol. 179(3), pages 665-682, September.
    7. Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    8. Montserrat Guillen & Ana M. Pérez-Marín & Mercedes Ayuso & Jens Perch Nielsen, 2018. "“Exposure to risk increases the excess of zero accident claims frequency in automobile insurance”," IREA Working Papers 201810, University of Barcelona, Research Institute of Applied Economics, revised May 2018.
    9. Meng, Shengwang & Gao, Yaqian & Huang, Yifan, 2022. "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 115-127.
    10. Germ`a Coenders & N'uria Arimany Serrat, 2023. "Accounting statement analysis at industry level. A gentle introduction to the compositional approach," Papers 2305.16842, arXiv.org, revised Feb 2024.
    11. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    12. Christian Eckert & Christof Neunsinger & Katrin Osterrieder, 2022. "Managing customer satisfaction: digital applications for insurance companies," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 569-602, July.
    13. Boonen, Tim J. & Guillen, Montserrat & Santolino, Miguel, 2019. "Forecasting compositional risk allocations," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 79-86.
    14. Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024. "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers 2401.16723, arXiv.org.
    15. Kevin Kuo & Daniel Lupton, 2020. "Towards Explainability of Machine Learning Models in Insurance Pricing," Papers 2003.10674, arXiv.org.
    16. Banghee So & Jean-Philippe Boucher & Emiliano A. Valdez, 2021. "Synthetic Dataset Generation of Driver Telematics," Risks, MDPI, vol. 9(4), pages 1-19, March.
    17. Donatella Porrini & Giulio Fusco & Cosimo Magazzino, 2020. "Black boxes and market efficiency: the effect on premiums in the Italian motor-vehicle insurance market," European Journal of Law and Economics, Springer, vol. 49(3), pages 455-472, June.
    18. Jennifer S. K. Chan & S. T. Boris Choy & Udi Makov & Ariel Shamir & Vered Shapovalov, 2022. "Variable Selection Algorithm for a Mixture of Poisson Regression for Handling Overdispersion in Claims Frequency Modeling Using Telematics Car Driving Data," Risks, MDPI, vol. 10(4), pages 1-10, April.
    19. Verschuren, Robert Matthijs, 2022. "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 379-392.
    20. Jean-Philippe Boucher & Roxane Turcotte, 2020. "A Longitudinal Analysis of the Impact of Distance Driven on the Probability of Car Accidents," Risks, MDPI, vol. 8(3), pages 1-19, September.
    21. Sojung Kim & Marcel Kleiber & Stefan Weber, 2022. "Microscopic Traffic Models, Accidents, and Insurance Losses," Papers 2208.12530, arXiv.org, revised Nov 2023.
    22. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    23. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
    24. Montserrat Guillen & Jens Perch Nielsen & Mercedes Ayuso & Ana M. Pérez‐Marín, 2019. "The Use of Telematics Devices to Improve Automobile Insurance Rates," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 662-672, March.
    25. Guangyuan Gao & Mario V. Wüthrich, 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data," Risks, MDPI, vol. 7(1), pages 1-18, January.
    26. Denuit, Michel & Guillen, Montserrat & Trufin, Julien, 2018. "Multivariate credibility modeling for usage-based motor insurance pricing with behavioral data," LIDAM Discussion Papers ISBA 2018032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    27. Gao, Guangyuan & Wüthrich, Mario V. & Yang, Hanfang, 2019. "Evaluation of driving risk at different speeds," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 108-119.

  9. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," BAFFI CAREFIN Working Papers 1505, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    2. Wong, Jackie S.T. & Forster, Jonathan J. & Smith, Peter W.F., 2018. "Bayesian mortality forecasting with overdispersion," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 206-221.
    3. Hong Li & Yang Lu, 2018. "A Bayesian non-parametric model for small population mortality," Post-Print hal-02419000, HAL.
    4. Peter Jevtic & Luca Regis, 2016. "A continuous-time stochastic model for the mortality surface of multiple populations," Working Papers 03/2016, IMT School for Advanced Studies Lucca, revised Jul 2016.
    5. Barigou, Karim & Goffard, Pierre-Olivier & Loisel, Stéphane & Salhi, Yahia, 2023. "Bayesian model averaging for mortality forecasting using leave-future-out validation," International Journal of Forecasting, Elsevier, vol. 39(2), pages 674-690.
    6. Yaser Awad & Shaul K. Bar-Lev & Udi Makov, 2022. "A New Class of Counting Distributions Embedded in the Lee–Carter Model for Mortality Projections: A Bayesian Approach," Risks, MDPI, vol. 10(6), pages 1-17, May.
    7. Kira Henshaw & Waleed Hana & Corina Constantinescu & Dalia Khalil, 2023. "Dependence Modelling of Lifetimes in Egyptian Families," Risks, MDPI, vol. 11(1), pages 1-25, January.
    8. Wang, Pengjie & Pantelous, Athanasios A. & Vahid, Farshid, 2023. "Multi-population mortality projection: The augmented common factor model with structural breaks," International Journal of Forecasting, Elsevier, vol. 39(1), pages 450-469.
    9. Patrizio Vanella & Ugofilippo Basellini & Berit Lange, 2020. "Assessing Excess Mortality in Times of Pandemics Based on Principal Component Analysis of Weekly Mortality Data -- The Case of COVID-19," Working Papers axbhmxrs-o0viyh9z07m, French Institute for Demographic Studies.
    10. Wang, Ling & Chiu, Mei Choi & Wong, Hoi Ying, 2021. "Volterra mortality model: Actuarial valuation and risk management with long-range dependence," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 1-14.
    11. Ling Wang & Mei Choi Chiu & Hoi Ying Wong, 2020. "Volterra mortality model: Actuarial valuation and risk management with long-range dependence," Papers 2009.09572, arXiv.org.

  10. Pigeon, Mathieu & Antonio, Katrien & Denuit, Michel, 2014. "Individual loss reserving using paid-incurred data," LIDAM Discussion Papers ISBA 2014014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Jonas Crevecoeur & Katrien Antonio & Roel Verbelen, 2018. "Modeling the number of hidden events subject to observation delay," Papers 1801.02935, arXiv.org, revised Mar 2019.
    2. Huang, Jinlong & Wu, Xianyi & Zhou, Xian, 2016. "Asymptotic behaviors of stochastic reserving: Aggregate versus individual models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 657-666.
    3. Maciak, Matúš & Okhrin, Ostap & Pešta, Michal, 2021. "Infinitely stochastic micro reserving," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 30-58.
    4. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Yang, Xinda, 2021. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 9-24.
    5. Arthur Charpentier & Mathieu Pigeon, 2016. "Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)," Risks, MDPI, vol. 4(2), pages 1-18, May.
    6. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2022. "Mack-Net model: Blending Mack's model with Recurrent Neural Networks," Papers 2205.07334, arXiv.org.
    7. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    8. Marie Michaelides & Mathieu Pigeon & H'el`ene Cossette, 2022. "Individual Claims Reserving using Activation Patterns," Papers 2208.08430, arXiv.org, revised Aug 2023.
    9. Mammen, Enno & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2015. "In-sample forecasting applied to reserving and mesothelioma mortality," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 76-86.
    10. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020. "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers 2008.07564, arXiv.org.
    11. Lopez, Olivier, 2019. "A censored copula model for micro-level claim reserving," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 1-14.
    12. Avanzi, Benjamin & Wong, Bernard & Yang, Xinda, 2016. "A micro-level claim count model with overdispersion and reporting delays," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 1-14.
    13. Fersini, Paola & Melisi, Giuseppe, 2016. "Stochastic model to evaluate the fair value of motor third-party liability under the direct reimbursement scheme and quantification of the capital requirement in a Solvency II perspective," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 27-44.
    14. Benjamin Avanzi & Gregory Clive Taylor & Bernard Wong & Xinda Yang, 2020. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Papers 2004.11169, arXiv.org, revised Dec 2020.
    15. Yixing Zhao & Rogemar Mamon & Heng Xiong, 2021. "Claim reserving for insurance contracts in line with the International Financial Reporting Standards 17: a new paid-incurred chain approach to risk adjustments," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    16. Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2019. "Infinitely Stochastic Micro Forecasting," Papers 1908.10636, arXiv.org, revised Sep 2019.
    17. Denuit, Michel & Trufin, Julien, 2016. "Collective Loss Reserving with Two Types of Claims in Motor Third Party Liability Insurance," LIDAM Discussion Papers ISBA 2016029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    18. Yanez, Juan Sebastian & Pigeon, Mathieu, 2021. "Micro-level parametric duration-frequency-severity modeling for outstanding claim payments," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 106-119.

  11. Pigeon, Mathieu & Antonio, Katrien & Denuit, Michel, 2014. "Individual loss reserving using paid–incurred data," LIDAM Reprints ISBA 2014024, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Jonas Crevecoeur & Katrien Antonio & Roel Verbelen, 2018. "Modeling the number of hidden events subject to observation delay," Papers 1801.02935, arXiv.org, revised Mar 2019.
    2. Huang, Jinlong & Wu, Xianyi & Zhou, Xian, 2016. "Asymptotic behaviors of stochastic reserving: Aggregate versus individual models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 657-666.
    3. Maciak, Matúš & Okhrin, Ostap & Pešta, Michal, 2021. "Infinitely stochastic micro reserving," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 30-58.
    4. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Yang, Xinda, 2021. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 9-24.
    5. Arthur Charpentier & Mathieu Pigeon, 2016. "Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)," Risks, MDPI, vol. 4(2), pages 1-18, May.
    6. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2022. "Mack-Net model: Blending Mack's model with Recurrent Neural Networks," Papers 2205.07334, arXiv.org.
    7. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    8. Marie Michaelides & Mathieu Pigeon & H'el`ene Cossette, 2022. "Individual Claims Reserving using Activation Patterns," Papers 2208.08430, arXiv.org, revised Aug 2023.
    9. Mammen, Enno & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2015. "In-sample forecasting applied to reserving and mesothelioma mortality," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 76-86.
    10. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020. "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers 2008.07564, arXiv.org.
    11. Ihsan Chaoubi & Camille Besse & H'el`ene Cossette & Marie-Pier C^ot'e, 2022. "Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network," Papers 2201.13267, arXiv.org.
    12. Lopez, Olivier, 2019. "A censored copula model for micro-level claim reserving," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 1-14.
    13. Avanzi, Benjamin & Wong, Bernard & Yang, Xinda, 2016. "A micro-level claim count model with overdispersion and reporting delays," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 1-14.
    14. Fersini, Paola & Melisi, Giuseppe, 2016. "Stochastic model to evaluate the fair value of motor third-party liability under the direct reimbursement scheme and quantification of the capital requirement in a Solvency II perspective," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 27-44.
    15. Benjamin Avanzi & Gregory Clive Taylor & Bernard Wong & Xinda Yang, 2020. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Papers 2004.11169, arXiv.org, revised Dec 2020.
    16. Yixing Zhao & Rogemar Mamon & Heng Xiong, 2021. "Claim reserving for insurance contracts in line with the International Financial Reporting Standards 17: a new paid-incurred chain approach to risk adjustments," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    17. Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2019. "Infinitely Stochastic Micro Forecasting," Papers 1908.10636, arXiv.org, revised Sep 2019.
    18. Yanez, Juan Sebastian & Pigeon, Mathieu, 2021. "Micro-level parametric duration-frequency-severity modeling for outstanding claim payments," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 106-119.

  12. Pigeon, Mathieu & Antonio, Katrien & Denuit, Michel, 2013. "Individual Loss Reserving with the Multivariate Skew Normal Framework," LIDAM Reprints ISBA 2013028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Jonas Crevecoeur & Katrien Antonio & Roel Verbelen, 2018. "Modeling the number of hidden events subject to observation delay," Papers 1801.02935, arXiv.org, revised Mar 2019.
    2. Huang, Jinlong & Wu, Xianyi & Zhou, Xian, 2016. "Asymptotic behaviors of stochastic reserving: Aggregate versus individual models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 657-666.
    3. Łukasz Delong & Mario V. Wüthrich, 2020. "Neural Networks for the Joint Development of Individual Payments and Claim Incurred," Risks, MDPI, vol. 8(2), pages 1-34, April.
    4. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Yang, Xinda, 2021. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 9-24.
    5. Francis Duval & Mathieu Pigeon, 2019. "Individual Loss Reserving Using a Gradient Boosting-Based Approach," Risks, MDPI, vol. 7(3), pages 1-18, July.
    6. Peng Shi & Glenn M. Fung & Daniel Dickinson, 2022. "Assessing hail risk for property insurers with a dependent marked point process," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 302-328, January.
    7. Richard J. Verrall & Mario V. Wüthrich, 2016. "Understanding Reporting Delay in General Insurance," Risks, MDPI, vol. 4(3), pages 1-36, July.
    8. Pigeon, Mathieu & Antonio, Katrien & Denuit, Michel, 2014. "Individual loss reserving using paid–incurred data," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 121-131.
    9. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    10. Marie Michaelides & Mathieu Pigeon & H'el`ene Cossette, 2022. "Individual Claims Reserving using Activation Patterns," Papers 2208.08430, arXiv.org, revised Aug 2023.
    11. Mammen, Enno & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2015. "In-sample forecasting applied to reserving and mesothelioma mortality," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 76-86.
    12. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020. "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers 2008.07564, arXiv.org.
    13. Emmanuel Jordy Menvouta & Jolien Ponnet & Robin Van Oirbeek & Tim Verdonck, 2022. "mCube: Multinomial Micro-level reserving Model," Papers 2212.00101, arXiv.org.
    14. Greg Taylor, 2019. "Loss Reserving Models: Granular and Machine Learning Forms," Risks, MDPI, vol. 7(3), pages 1-18, July.
    15. Ihsan Chaoubi & Camille Besse & H'el`ene Cossette & Marie-Pier C^ot'e, 2022. "Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network," Papers 2201.13267, arXiv.org.
    16. Avanzi, Benjamin & Wong, Bernard & Yang, Xinda, 2016. "A micro-level claim count model with overdispersion and reporting delays," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 1-14.
    17. Matias Leppisaari, 2013. "Modeling catastrophic deaths using EVT with a microsimulation approach to reinsurance pricing," Papers 1310.8604, arXiv.org.
    18. Alexandre Brouste & Christophe Dutang, 2016. "Closed-form and numerical computations of actuarial indicators in ruin theory and claim reserving," Post-Print hal-01616192, HAL.
    19. Fersini, Paola & Melisi, Giuseppe, 2016. "Stochastic model to evaluate the fair value of motor third-party liability under the direct reimbursement scheme and quantification of the capital requirement in a Solvency II perspective," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 27-44.
    20. Benjamin Avanzi & Gregory Clive Taylor & Bernard Wong & Xinda Yang, 2020. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Papers 2004.11169, arXiv.org, revised Dec 2020.
    21. Gao, Lisa & Shi, Peng, 2022. "Leveraging high-resolution weather information to predict hail damage claims: A spatial point process for replicated point patterns," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 161-179.
    22. Andrea Gabrielli & Mario V. Wüthrich, 2018. "An Individual Claims History Simulation Machine," Risks, MDPI, vol. 6(2), pages 1-32, March.
    23. Rendao Ye & Bingni Fang & Weixiao Du & Kun Luo & Yiting Lu, 2022. "Bootstrap Tests for the Location Parameter under the Skew-Normal Population with Unknown Scale Parameter and Skewness Parameter," Mathematics, MDPI, vol. 10(6), pages 1-23, March.
    24. Denuit, Michel & Trufin, Julien, 2016. "Collective Loss Reserving with Two Types of Claims in Motor Third Party Liability Insurance," LIDAM Discussion Papers ISBA 2016029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    25. Yanez, Juan Sebastian & Pigeon, Mathieu, 2021. "Micro-level parametric duration-frequency-severity modeling for outstanding claim payments," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 106-119.

Articles

  1. Crevecoeur, Jonas & Antonio, Katrien & Desmedt, Stijn & Masquelein, Alexandre, 2023. "Bridging the gap between pricing and reserving with an occurrence and development model for non-life insurance claims," ASTIN Bulletin, Cambridge University Press, vol. 53(2), pages 185-212, May.
    See citations under working paper version above.
  2. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    See citations under working paper version above.
  3. Henckaerts, Roel & Antonio, Katrien, 2022. "The added value of dynamically updating motor insurance prices with telematics collected driving behavior data," Insurance: Mathematics and Economics, Elsevier, vol. 105(C), pages 79-95.

    Cited by:

    1. Chih-Te Yang & Yensen Ni & Mu-Hsiang Yu & Yuhsin Chen & Paoyu Huang, 2023. "Decoding the Profitability of Insurance Products: A Novel Approach to Evaluating Non-Participating and Participating Insurance Policies," Mathematics, MDPI, vol. 11(13), pages 1-16, June.
    2. Marian Reiff & Erik Šoltés & Silvia Komara & Tatiana Šoltésová & Silvia Zelinová, 2022. "Segmentation and estimation of claim severity in motor third-party liability insurance through contrast analysis," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 803-842, September.
    3. Sojung Kim & Marcel Kleiber & Stefan Weber, 2022. "Microscopic Traffic Models, Accidents, and Insurance Losses," Papers 2208.12530, arXiv.org, revised Nov 2023.

  4. Deresa, N.W. & Van Keilegom, I. & Antonio, K., 2022. "Copula-based inference for bivariate survival data with left truncation and dependent censoring," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 1-21.

    Cited by:

    1. Janette Larney & James Samuel Allison & Gerrit Lodewicus Grobler & Marius Smuts, 2023. "Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty," Mathematics, MDPI, vol. 11(10), pages 1-17, May.

  5. Jens Robben & Katrien Antonio & Sander Devriendt, 2022. "Assessing the Impact of the COVID-19 Shock on a Stochastic Multi-Population Mortality Model," Risks, MDPI, vol. 10(2), pages 1-33, January.
    See citations under working paper version above.
  6. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.

    Cited by:

    1. Zhu, Ying & Xia, Tangbin & Chen, Zhen & Pan, Ershun & Xi, Lifeng, 2022. "Optimal maintenance service strategy for OEM entering competitive MRO market under opposite patterns," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2023. "Empirical risk assessment of maintenance costs under full-service contracts," European Journal of Operational Research, Elsevier, vol. 304(2), pages 476-493.
    3. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
    4. Li, Ting & He, Shuguang & Zhao, Xiujie & Liu, Bin, 2023. "Warranty service contracts design for deteriorating products with maintenance duration commitments," International Journal of Production Economics, Elsevier, vol. 264(C).

  7. Roel Henckaerts & Marie-Pier Côté & Katrien Antonio & Roel Verbelen, 2021. "Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(2), pages 255-285, April.

    Cited by:

    1. Freek Holvoet & Katrien Antonio & Roel Henckaerts, 2023. "Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff," Papers 2310.12671, arXiv.org, revised Oct 2023.
    2. Zuleyka Díaz Martínez & José Fernández Menéndez & Luis Javier García Villalba, 2023. "Tariff Analysis in Automobile Insurance: Is It Time to Switch from Generalized Linear Models to Generalized Additive Models?," Mathematics, MDPI, vol. 11(18), pages 1-16, September.
    3. Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024. "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers 2401.16723, arXiv.org.
    4. Benjamin Avanzi & Greg Taylor & Melantha Wang & Bernard Wong, 2023. "Machine Learning with High-Cardinality Categorical Features in Actuarial Applications," Papers 2301.12710, arXiv.org.
    5. Marian Reiff & Erik Šoltés & Silvia Komara & Tatiana Šoltésová & Silvia Zelinová, 2022. "Segmentation and estimation of claim severity in motor third-party liability insurance through contrast analysis," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 803-842, September.
    6. Verschuren, Robert Matthijs, 2022. "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 379-392.
    7. Thomas Poufinas & Periklis Gogas & Theophilos Papadimitriou & Emmanouil Zaganidis, 2023. "Machine Learning in Forecasting Motor Insurance Claims," Risks, MDPI, vol. 11(9), pages 1-19, September.
    8. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
    9. Catalina Lozano-Murcia & Francisco P. Romero & Jesus Serrano-Guerrero & Jose A. Olivas, 2023. "A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    10. Codruţa Mare & Daniela Manaţe & Gabriela-Mihaela Mureşan & Simona Laura Dragoş & Cristian Mihai Dragoş & Alexandra-Anca Purcel, 2022. "Machine Learning Models for Predicting Romanian Farmers’ Purchase of Crop Insurance," Mathematics, MDPI, vol. 10(19), pages 1-13, October.
    11. Hu, Changyue & Quan, Zhiyu & Chong, Wing Fung, 2022. "Imbalanced learning for insurance using modified loss functions in tree-based models," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 13-32.
    12. Gao, Lisa & Shi, Peng, 2022. "Leveraging high-resolution weather information to predict hail damage claims: A spatial point process for replicated point patterns," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 161-179.
    13. Gu, Zheng & Li, Yunxian & Zhang, Minghui & Liu, Yifei, 2023. "Modelling economic losses from earthquakes using regression forests: Application to parametric insurance," Economic Modelling, Elsevier, vol. 125(C).

  8. Devriendt, Sander & Antonio, Katrien & Reynkens, Tom & Verbelen, Roel, 2021. "Sparse regression with Multi-type Regularized Feature modeling," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 248-261.

    Cited by:

    1. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.

  9. Crevecoeur, Jonas & Antonio, Katrien & Verbelen, Roel, 2019. "Modeling the number of hidden events subject to observation delay," European Journal of Operational Research, Elsevier, vol. 277(3), pages 930-944.
    See citations under working paper version above.
  10. Roel Henckaerts & Katrien Antonio & Maxime Clijsters & Roel Verbelen, 2018. "A data driven binning strategy for the construction of insurance tariff classes," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2018(8), pages 681-705, September.
    See citations under working paper version above.
  11. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    See citations under working paper version above.
  12. Reynkens, Tom & Verbelen, Roel & Beirlant, Jan & Antonio, Katrien, 2017. "Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 65-77.
    See citations under working paper version above.
  13. Dhaene, Jan & Godecharle, Els & Antonio, Katrien & Denuit, Michel & Hanbali, Hamza, 2017. "Lifelong Health Insurance Covers With Surrender Values: Updating Mechanisms In The Presence Of Medical Inflation," ASTIN Bulletin, Cambridge University Press, vol. 47(3), pages 803-836, September.
    See citations under working paper version above.
  14. van Berkum, Frank & Antonio, Katrien & Vellekoop, Michel, 2017. "A Bayesian Joint Model For Population And Portfolio-Specific Mortality," ASTIN Bulletin, Cambridge University Press, vol. 47(3), pages 681-713, September.

    Cited by:

    1. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    2. Hong Li & Yang Lu, 2018. "A Bayesian non-parametric model for small population mortality," Post-Print hal-02419000, HAL.
    3. Jackie Li & Atsuyuki Kogure, 2021. "Bayesian Mixture Modelling for Mortality Projection," Risks, MDPI, vol. 9(4), pages 1-12, April.

  15. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2016. "Multivariate mixtures of Erlangs for density estimation under censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 429-455, July.

    Cited by:

    1. Tom Reynkens & Roel Verbelen & Jan Beirlant & Katrien Antonio, 2016. "Modeling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Working Papers of Department of Decision Sciences and Information Management, Leuven 549545, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
    2. Mohammed, Nawaf & Furman, Edward & Su, Jianxi, 2021. "Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of conditional tail expectation," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 425-436.
    3. Nawaf Mohammed & Edward Furman & Jianxi Su, 2021. "Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of Conditional Tail Expectation," Papers 2102.05003, arXiv.org, revised Aug 2021.
    4. Miljkovic, Tatjana & Grün, Bettina, 2016. "Modeling loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 387-396.
    5. Yin, Cuihong & Sheldon Lin, X. & Huang, Rongtan & Yuan, Haili, 2019. "On the consistency of penalized MLEs for Erlang mixtures," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 12-20.

  16. Frank van Berkum & Katrien Antonio & Michel Vellekoop, 2016. "The impact of multiple structural changes on mortality predictions," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2016(7), pages 581-603, August.

    Cited by:

    1. Huijing Li & Rui Zhou & Min Ji, 2023. "Nonlinear Modeling of Mortality Data and Its Implications for Longevity Bond Pricing," Risks, MDPI, vol. 11(12), pages 1-25, November.
    2. Barigou, Karim & Goffard, Pierre-Olivier & Loisel, Stéphane & Salhi, Yahia, 2023. "Bayesian model averaging for mortality forecasting using leave-future-out validation," International Journal of Forecasting, Elsevier, vol. 39(2), pages 674-690.

  17. Els Godecharle & Katrien Antonio, 2015. "Reserving by Conditioning on Markers of Individual Claims: A Case Study Using Historical Simulation," North American Actuarial Journal, Taylor & Francis Journals, vol. 19(4), pages 273-288, October.

    Cited by:

    1. Jonas Crevecoeur & Katrien Antonio & Roel Verbelen, 2018. "Modeling the number of hidden events subject to observation delay," Papers 1801.02935, arXiv.org, revised Mar 2019.
    2. Maciak, Matúš & Okhrin, Ostap & Pešta, Michal, 2021. "Infinitely stochastic micro reserving," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 30-58.
    3. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    4. Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2019. "Infinitely Stochastic Micro Forecasting," Papers 1908.10636, arXiv.org, revised Sep 2019.

  18. Verbelen, Roel & Gong, Lan & Antonio, Katrien & Badescu, Andrei & Lin, Sheldon, 2015. "Fitting Mixtures Of Erlangs To Censored And Truncated Data Using The Em Algorithm," ASTIN Bulletin, Cambridge University Press, vol. 45(3), pages 729-758, September.

    Cited by:

    1. Fung, Tsz Chai, 2022. "Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 180-198.
    2. Alessandro Staino & Emilio Russo & Massimo Costabile & Arturo Leccadito, 2023. "Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint," Computational Management Science, Springer, vol. 20(1), pages 1-32, December.
    3. Tom Reynkens & Roel Verbelen & Jan Beirlant & Katrien Antonio, 2016. "Modeling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Working Papers of Department of Decision Sciences and Information Management, Leuven 549545, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
    4. Delong, Łukasz & Lindholm, Mathias & Wüthrich, Mario V., 2021. "Gamma Mixture Density Networks and their application to modelling insurance claim amounts," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 240-261.
    5. Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2023. "Hidden semi-Markov models for rainfall-related insurance claims," Discussion Papers 2023/17, Norwegian School of Economics, Department of Business and Management Science.
    6. Sebastian Calcetero-Vanegas & Andrei L. Badescu & X. Sheldon Lin, 2023. "Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method," Papers 2307.10808, arXiv.org, revised Jul 2023.
    7. Fung, Tsz Chai & Badescu, Andrei L. & Lin, X. Sheldon, 2019. "A class of mixture of experts models for general insurance: Theoretical developments," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 111-127.
    8. Mingxing He & Jiahua Chen, 2022. "Strong consistency of the MLE under two-parameter Gamma mixture models with a structural scale parameter," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 125-154, March.
    9. Luis Rincón & David J. Santana, 2022. "Ruin Probability for Finite Erlang Mixture Claims Via Recurrence Sequences," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 2213-2236, September.
    10. Semhar Michael & Tatjana Miljkovic & Volodymyr Melnykov, 2020. "Mixture modeling of data with multiple partial right-censoring levels," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 355-378, June.
    11. Počuča, Nikola & Jevtić, Petar & McNicholas, Paul D. & Miljkovic, Tatjana, 2020. "Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 79-93.
    12. Blostein, Martin & Miljkovic, Tatjana, 2019. "On modeling left-truncated loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 35-46.
    13. Bhati, Deepesh & Ravi, Sreenivasan, 2018. "On generalized log-Moyal distribution: A new heavy tailed size distribution," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 247-259.
    14. Miljkovic, Tatjana & Grün, Bettina, 2016. "Modeling loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 387-396.
    15. Yin, Cuihong & Sheldon Lin, X. & Huang, Rongtan & Yuan, Haili, 2019. "On the consistency of penalized MLEs for Erlang mixtures," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 12-20.
    16. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2016. "Multivariate mixtures of Erlangs for density estimation under censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 429-455, July.

  19. Pigeon, Mathieu & Antonio, Katrien & Denuit, Michel, 2014. "Individual loss reserving using paid–incurred data," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 121-131.
    See citations under working paper version above.
  20. Katrien Antonio & Richard Plat, 2014. "Micro-level stochastic loss reserving for general insurance," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2014(7), pages 649-669.

    Cited by:

    1. Sebastian Calcetero-Vanegas & Andrei L. Badescu & X. Sheldon Lin, 2023. "Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method," Papers 2307.10808, arXiv.org, revised Jul 2023.

  21. Pigeon, Mathieu & Antonio, Katrien & Denuit, Michel, 2013. "Individual Loss Reserving With The Multivariate Skew Normal Framework," ASTIN Bulletin, Cambridge University Press, vol. 43(3), pages 399-428, September.
    See citations under working paper version above.
  22. Katrien Antonio & Emiliano Valdez, 2012. "Statistical concepts of a priori and a posteriori risk classification in insurance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 187-224, June.

    Cited by:

    1. Mihaela DAVID, 2014. "Modeling The Frequency Of Claims In Auto Insurance With Application To A French Case," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 13, pages 69-85, June.
    2. Anna Szymańska, 2015. "Impact of the number of classes and transition rules of bonus-malus system on its efficiency in tariff setting," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 37, pages 253-268.
    3. Yves Staudt & Joël Wagner, 2021. "Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance," Risks, MDPI, vol. 9(3), pages 1-28, March.
    4. Simon Sølvsten & Brooks Kaiser, 2023. "Do insurers adjust prices for the adoption of loss prevention technologies? Evidence from Danish municipal contracts," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 26(1), pages 57-82, March.
    5. Jeong, Himchan & Valdez, Emiliano A., 2020. "Predictive compound risk models with dependence," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 182-195.
    6. Lee, Woojoo & Kim, Jeonghwan & Ahn, Jae Youn, 2020. "The Poisson random effect model for experience ratemaking: Limitations and alternative solutions," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 26-36.
    7. Verschuren, Robert Matthijs, 2022. "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 379-392.
    8. Mihaela Covrig & Iulian Mircea & Gheorghita Zbaganu & Alexandru Coser & Alexandru Tindeche, 2015. "Using R In Generalized Linear Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 33-45, September.
    9. Ramon Alemany & Catalina Bolance & Montserrat Guillen, 2014. "Accounting for severity of risk when pricing insurance products," Working Papers 2014-05, Universitat de Barcelona, UB Riskcenter.
    10. Levon Barseghyan & Francesca Molinari & Darcy Steeg Morris & Joshua C. Teitelbaum, 2020. "The Cost of Legal Restrictions on Experience Rating," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(1), pages 38-70, March.
    11. Adlane Haffar & Éric Le Fur & Mohamed Khordj, 2023. "Securitization of pandemic risk by using coronabond," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 209-229, June.
    12. Serpil Bülbül & Kemal Baykal, 2016. "Optimal Bonus-Malus System Design in Motor Third-Party Liability Insurance in Turkey: Negative Binomial Model," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(8), pages 205-205, August.

  23. Antonio, Katrien & Frees, Edward W. & Valdez, Emiliano A., 2010. "A Multilevel Analysis of Intercompany Claim Counts," ASTIN Bulletin, Cambridge University Press, vol. 40(1), pages 151-177, May.

    Cited by:

    1. Peng Shi, 2017. "A Multivariate Analysis of Intercompany Loss Triangles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(2), pages 717-737, June.
    2. Desjardins, Denise & Dionne, Georges & Lu, Yang, 2021. "Hierarchical random effects model for insurance pricing of vehicles belonging to a fleet," Working Papers 21-2, HEC Montreal, Canada Research Chair in Risk Management.
    3. Freek Holvoet & Katrien Antonio & Roel Henckaerts, 2023. "Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff," Papers 2310.12671, arXiv.org, revised Oct 2023.
    4. Mihaela DAVID, 2014. "Modeling The Frequency Of Claims In Auto Insurance With Application To A French Case," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 13, pages 69-85, June.
    5. Gaurav Khemka & Steven Roberts & Timothy Higgins, 2017. "The Impact of Changes to the Unemployment Rate on Australian Disability Income Insurance Claim Incidence," Risks, MDPI, vol. 5(1), pages 1-18, March.
    6. Pechon, Florian & Denuit, Michel & Trufin, Julien, 2019. "Home and Motor insurance joined at a household level using multivariate credibility," LIDAM Discussion Papers ISBA 2019013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Katrien Antonio & Emiliano Valdez, 2012. "Statistical concepts of a priori and a posteriori risk classification in insurance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 187-224, June.
    8. Zhao, Xiaobing & Zhou, Xian, 2012. "Copula models for insurance claim numbers with excess zeros and time-dependence," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 191-199.

  24. Katrien Antonio & Jan Beirlant, 2008. "Issues in Claims Reserving and Credibility: A Semiparametric Approach With Mixed Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 643-676, September.

    Cited by:

    1. Sarabia, José María & Guillén, Montserrat, 2008. "Joint modelling of the total amount and the number of claims by conditionals," Insurance: Mathematics and Economics, Elsevier, vol. 43(3), pages 466-473, December.
    2. Peng Shi, 2017. "A Multivariate Analysis of Intercompany Loss Triangles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(2), pages 717-737, June.
    3. Zuleyka Díaz Martínez & José Fernández Menéndez & Luis Javier García Villalba, 2023. "Tariff Analysis in Automobile Insurance: Is It Time to Switch from Generalized Linear Models to Generalized Additive Models?," Mathematics, MDPI, vol. 11(18), pages 1-16, September.
    4. Benjamin Avanzi & Xingyun Tan & Greg Taylor & Bernard Wong, 2023. "Cyber Insurance Risk: Reporting Delays, Third-Party Cyber Events, and Changes in Reporting Propensity -- An Analysis Using Data Breaches Published by U.S. State Attorneys General," Papers 2310.04786, arXiv.org.
    5. Lally, Nathan & Hartman, Brian, 2018. "Estimating loss reserves using hierarchical Bayesian Gaussian process regression with input warping," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 124-140.
    6. Tingting Chen & Anthony Francis Desmond & Peter Adamic, 2023. "Generalized Additive Modelling of Dependent Frequency and Severity Distributions for Aggregate Claims," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 12(4), pages 1-1.
    7. Ozkok, Erengul & Streftaris, George & Waters, Howard R. & Wilkie, A. David, 2012. "Bayesian modelling of the time delay between diagnosis and settlement for Critical Illness Insurance using a Burr generalised-linear-type model," Insurance: Mathematics and Economics, Elsevier, vol. 50(2), pages 266-279.
    8. Björkwall, Susanna & Hössjer, Ola & Ohlsson, Esbjörn & Verrall, Richard, 2011. "A generalized linear model with smoothing effects for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 49(1), pages 27-37, July.
    9. Yanwei Zhang & Vanja Dukic, 2013. "Predicting Multivariate Insurance Loss Payments Under the Bayesian Copula Framework," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(4), pages 891-919, December.
    10. Stefano Cavastracci Strascia & Agostino Tripodi, 2018. "Overdispersed-Poisson Model in Claims Reserving: Closed Tool for One-Year Volatility in GLM Framework," Risks, MDPI, vol. 6(4), pages 1-24, December.

  25. Antonio, Katrien & Beirlant, Jan, 2007. "Actuarial statistics with generalized linear mixed models," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 58-76, January.

    Cited by:

    1. Mahani, Alireza S. & Sharabiani, Mansour T.A., 2015. "SIMD parallel MCMC sampling with applications for big-data Bayesian analytics," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 75-99.
    2. Murwan H. M. A. Siddig, 2016. "Application of the Generalized Linear Models in Actuarial Framework," Papers 1611.02556, arXiv.org.
    3. Alicja Wolny-Dominiak & Tomasz Żądło, 2021. "The Measures of Accuracy of Claim Frequency Credibility Predictor," Sustainability, MDPI, vol. 13(21), pages 1-13, October.
    4. Pitselis, Georgios & Grigoriadou, Vasiliki & Badounas, Ioannis, 2015. "Robust loss reserving in a log-linear model," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 14-27.
    5. Katrien Antonio & Jan Beirlant, 2008. "Issues in Claims Reserving and Credibility: A Semiparametric Approach With Mixed Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 643-676, September.
    6. Fabienne Comte & Celine Duval & Valentine Genon-Catalot & Johanna Kappus, 2015. "Estimation of the Jump Size Density in a Mixed Compound Poisson Process," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1023-1044, December.
    7. Hudecová, Šárka & Pešta, Michal, 2013. "Modeling dependencies in claims reserving with GEE," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 786-794.
    8. Gigante, Patrizia & Picech, Liviana & Sigalotti, Luciano, 2013. "Claims reserving in the hierarchical generalized linear model framework," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 381-390.
    9. Meyricke, Ramona & Sherris, Michael, 2013. "The determinants of mortality heterogeneity and implications for pricing annuities," Insurance: Mathematics and Economics, Elsevier, vol. 53(2), pages 379-387.
    10. Benjamin Avanzi & Greg Taylor & Melantha Wang & Bernard Wong, 2023. "Machine Learning with High-Cardinality Categorical Features in Actuarial Applications," Papers 2301.12710, arXiv.org.
    11. Norbert Paska, 2018. "Zastosowanie modeli ZINB GLMM z efektem losowym agenta w taryfikacji ubezpieczeń majątkowych," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 53, pages 63-76.
    12. Michal Gerthofer & Michal Pešta, 2017. "Stochastic Claims Reserving in Insurance Using Random Effects," Prague Economic Papers, Prague University of Economics and Business, vol. 2017(5), pages 542-560.
    13. Xie, Liang & Madden, Laurence V., 2014. "%HPGLIMMIX: A High-Performance SAS Macro for GLMM Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i08).
    14. Paulsen, Jostein & Lunde, Astrid & Skaug, Hans Julius, 2008. "Fitting mixed-effects models when data are left truncated," Insurance: Mathematics and Economics, Elsevier, vol. 43(1), pages 121-133, August.
    15. Katrien Antonio & Emiliano Valdez, 2012. "Statistical concepts of a priori and a posteriori risk classification in insurance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 187-224, June.
    16. Li-Hua Lai, 2016. "Dynamic modelling in loss frequency and severity estimated: Evidence from the agricultural rice loss due to typhoons in Taiwan," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 62(3), pages 113-123.
    17. Pešta, Michal & Okhrin, Ostap, 2014. "Conditional least squares and copulae in claims reserving for a single line of business," Insurance: Mathematics and Economics, Elsevier, vol. 56(C), pages 28-37.
    18. Dietsch, Michel & Petey, Joël, 2015. "The credit-risk implications of home ownership promotion: The effects of public subsidies and adjustable-rate loans," Journal of Housing Economics, Elsevier, vol. 28(C), pages 103-120.
    19. Shengkun Xie & Chong Gan, 2023. "Estimating Territory Risk Relativity Using Generalized Linear Mixed Models and Fuzzy C -Means Clustering," Risks, MDPI, vol. 11(6), pages 1-20, May.
    20. Silvie Kafková & Lenka Křivánková, 2014. "Generalized Linear Models in Vehicle Insurance," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 62(2), pages 383-388.
    21. Pigeon, Mathieu & Henry de Frahan, Bruno & Denuit, Michel, 2014. "Evaluation of the EU Proposed Farm Income Stabilisation Tool by Skew Normal Linear Mixed Models," LIDAM Discussion Papers ISBA 2014003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    22. Eguchi, Shoichi, 2018. "Model comparison for generalized linear models with dependent observations," Econometrics and Statistics, Elsevier, vol. 5(C), pages 171-188.
    23. Dornheim, Harald & Brazauskas, Vytaras, 2011. "Robust-efficient credibility models with heavy-tailed claims: A mixed linear models perspective," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 72-84, January.
    24. Meng Sun & Yi Lu, 2022. "A Generalized Linear Mixed Model for Data Breaches and Its Application in Cyber Insurance," Risks, MDPI, vol. 10(12), pages 1-23, November.
    25. Andreas Bayerstadler & Franz Benstetter & Christian Heumann & Fabian Winter, 2014. "A predictive modeling approach to increasing the economic effectiveness of disease management programs," Health Care Management Science, Springer, vol. 17(3), pages 284-301, September.
    26. Baumgartner, Carolin & Gruber, Lutz F. & Czado, Claudia, 2015. "Bayesian total loss estimation using shared random effects," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 194-201.
    27. Jan R. Landwehr & Andreas Herrmann & Mark Heitmann, 2009. "To be different or to be average? [Die Attraktivität des Durchschnittsprodukts]," Schmalenbach Journal of Business Research, Springer, vol. 61(3), pages 226-250, May.

  26. Katrien Antonio & Jan Beirlant & Tom Hoedemakers & Robert Verlaak, 2006. "Lognormal Mixed Models for Reported Claims Reserves," North American Actuarial Journal, Taylor & Francis Journals, vol. 10(1), pages 30-48.

    Cited by:

    1. Antonio, Katrien & Beirlant, Jan, 2007. "Actuarial statistics with generalized linear mixed models," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 58-76, January.
    2. Gigante, Patrizia & Picech, Liviana & Sigalotti, Luciano, 2013. "Claims reserving in the hierarchical generalized linear model framework," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 381-390.
    3. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020. "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers 2008.07564, arXiv.org.
    4. Paulsen, Jostein & Lunde, Astrid & Skaug, Hans Julius, 2008. "Fitting mixed-effects models when data are left truncated," Insurance: Mathematics and Economics, Elsevier, vol. 43(1), pages 121-133, August.
    5. Huang, Jinlong & Qiu, Chunjuan & Wu, Xianyi & Zhou, Xian, 2015. "An individual loss reserving model with independent reporting and settlement," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 232-245.
    6. Alicja Wolny-Dominiak, 2016. "The hierarchical generalized linear model and the bootstrap estimator of the error of prediction of loss reserves in a non-life insurance company," Papers 1612.04126, arXiv.org.

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