Approximate Bayesian computations to fit and compare insurance loss models

DOI10.1016/j.insmatheco.2021.06.002zbMath1471.91459arXiv2007.03833OpenAlexW3182517088MaRDI QIDQ2234770

Patrick J. Laub, Pierre-Olivier Goffard

Publication date: 19 October 2021

Published in: Insurance Mathematics \& Economics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.03833

zbMATH Keywords

Bayesian statisticscompound distributionsequential Monte CarloWasserstein distanceapproximate Bayesian computationlikelihood-free inferencestatistical claim modeling

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Numerical methods (including Monte Carlo methods) (91G60) Actuarial mathematics (91G05)

Related Items (3)

Cyber risk frequency, severity and insurance viability ⋮ Sequential Monte Carlo samplers to fit and compare insurance loss models ⋮ Discrepancy-based inference for intractable generative models using quasi-Monte Carlo

Uses Software

• CASdatasets
• PyMC
• BayesDA
• Python

Cites Work

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