@article{lopez19,
  title={A censored copula model for micro-level claim reserving},
  author={Lopez, Olivier},
  journal={Insurance: Mathematics and Economics},
  volume={\textbf{87}},
  pages={1--14},
  year={2019},
  publisher={Elsevier}
}

@article{harnau18,
  title={Over-dispersed age-period-cohort models},
  author={Harnau, Jonas and Nielsen, Bent},
  journal={Journal of the American Statistical Association},
  volume={113},
  number={524},
  pages={1722--1732},
  year={2018},
  publisher={Taylor \& Francis}
}

@article{england99,
  title={Analytic and bootstrap estimates of prediction errors in claims reserving},
  author={England, Peter and Verrall, Richard},
  journal={Insurance: mathematics and economics},
  volume={25},
  number={3},
  pages={281--293},
  year={1999},
  publisher={Elsevier}
}

@Manual{apcpackage,
    title = {apc: Age-Period-Cohort Analysis},
    author = {Zoe Fannon and Bent Nielsen},
    year = {2020},
    note = {R package version 2.0.0},
    url = {https://CRAN.R-project.org/package=apc},
  }

 @Manual{chainladderpackage,
    title = {ChainLadder: Statistical Methods and Models for Claims Reserving in General
Insurance},
    author = {Markus Gesmann and Daniel Murphy and Yanwei (Wayne) Zhang and Alessandro Carrato and Mario Wuthrich and Fabio Concina and Eric {Dal Moro}},
    year = {2025},
    note = {R package version 0.2.20},
    url = {https://CRAN.R-project.org/package=ChainLadder},
  }


@article{mack93,
  title={Distribution-free calculation of the standard error of chain ladder reserve estimates},
  author={Mack, Thomas},
  journal={ASTIN Bulletin: The Journal of the IAA},
  volume={\textbf{23}},
  number={2},
  pages={213--225},
  year={1993},
  publisher={Cambridge University Press}
}


@article{farewell82,
  title={The use of mixture models for the analysis of survival data with long-term survivors},
  author={Farewell, Vern T},
  journal={Biometrics},
  pages={1041--1046},
  year={1982},
  publisher={JSTOR}
}

@article{hiabu17,
  title={On the relationship between classical chain ladder and granular reserving},
  author={Munir Hiabu},
  journal={Scandinavian Actuarial Journal},
  year={2017},
  volume={2017},
  pages={708 - 729}
}


@article{bischofberger19,
  title={Continuous chain-ladder with paid data},
  author={Stephan M. Bischofberger and Munir Hiabu and Alexander Isakson},
  journal={Scandinavian Actuarial Journal},
  year={2019},
  volume={2020},
  pages={477 - 502}
}


@article{keiding98,
  title={The Cox Regression Model for Claims Data m Non-Life Insurance},
  author={Keiding, Niels and Andersen, Christian and Fledelius, Peter},
  journal={ASTIN Bulletin: The Journal of the IAA},
  volume={28},
  number={1},
  pages={95--118},
  year={1998},
  publisher={Cambridge University Press}
}


@article{wang96, title={Premium Calculation by Transforming the Layer Premium Density}, volume={26}, DOI={10.2143/AST.26.1.563234}, number={1}, journal={ASTIN Bulletin}, publisher={Cambridge University Press}, author={Wang, Shaun}, year={1996}, pages={71–92}}

@article{zhao10,
  title={Applying copula models to individual claim loss reserving methods},
  author={Zhao, XiaoBing and Zhou, Xian},
  journal={Insurance: Mathematics and Economics},
  volume={46},
  number={2},
  pages={290--299},
  year={2010},
  publisher={Elsevier}
}


@article{hofert11,
  title={Nested Archimedean copulas meet R: The nacopula package},
  author={Hofert, Marius and M{\"a}chler, Martin},
  journal={Journal of Statistical Software},
  volume={39},
  number={9},
  pages={1--20},
  year={2011},
  publisher={American Statistical Association}
}


@book{joe97,
  title={Multivariate models and multivariate dependence concepts},
  author={Joe, Harry},
  year={1997},
  publisher={CRC press}
}


@article{prentice81,
  title={On the regression analysis of multivariate failure time data},
  author={Prentice, Ross L and Williams, Benjamin J and Peterson, Arthur V},
  journal={Biometrika},
  volume={68},
  number={2},
  pages={373--379},
  year={1981},
  publisher={Oxford University Press}
}

@article{gao22,
  title={Boosting Poisson regression models with telematics car driving data},
  author={Gao, Guangyuan and Wang, He and W{\"u}thrich, Mario V},
  journal={Machine Learning},
  volume={111},
  number={1},
  pages={243--272},
  year={2022},
  publisher={Springer}
}

@article{pittarello23,
  title={Chain Ladder Plus: a versatile approach for claims reserving},
  author={Pittarello, Gabriele and Hiabu, Munir and Villegas, Andr{\'e}s M},
  journal={arXiv preprint arXiv:2301.03858},
  year={2023}
}

@article{hiabu23,
  title={A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving},
  author={Hiabu, Munir and Hofman, Emil and Pittarello, Gabriele},
  year={2023},
  journal={preprint}}

@article{ajne94,
title={Additivity of Chain-Ladder Projections},
volume={24},
number={2},
journal={ASTIN Bulletin},
publisher={Cambridge University Press},
author={Ajne, Björn},
year={1994},
pages={311–318}}

@article{snoek12,
  title={Practical bayesian optimization of machine learning algorithms},
  author={Snoek, Jasper and Larochelle, Hugo and Adams, Ryan P},
  journal={Advances in neural information processing systems},
  volume={25},
  year={2012}
}

@article{katzman18,
  title={DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network},
  author={Katzman, Jared L and Shaham, Uri and Cloninger, Alexander and Bates, Jonathan and Jiang, Tingting and Kluger, Yuval},
  journal={BMC medical research methodology},
  volume={18},
  number={1},
  pages={1--12},
  year={2018},
  publisher={BioMed Central}
}


@incollection{pytorch,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}


@book{hastie09,
  title={The elements of statistical learning: data mining, inference, and prediction},
  author={Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome H and Friedman, Jerome H},
  volume={2},
  year={2009},
  publisher={Springer}
}


@Manual{ParBayesianOptimization,
    title = {ParBayesianOptimization: Parallel Bayesian Optimization of Hyperparameters},
    author = {Samuel Wilson},
    year = {2022},
    note = {R package version 1.2.6},
    url = {https://CRAN.R-project.org/package=ParBayesianOptimization},
  }
  
  @article{cox72,
  title={Regression models and life-tables},
  author={Cox, David R},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={34},
  number={2},
  pages={187--202},
  year={1972},
  publisher={Wiley Online Library}
}


@article{pittarello25,
  title={Replicating and Extending Chain-Ladder via an Age--Period--Cohort Structure on the Claim Development in a Run-Off Triangle},
  author={Pittarello, Gabriele and Hiabu, Munir and Villegas, Andr{\'e}s M},
  journal={North American Actuarial Journal},
  pages={1--31},
  year={2025},
  publisher={Taylor \& Francis}
}
