Estimating loss reserves using hierarchical Bayesian Gaussian process regression with input warping

DOI10.1016/j.insmatheco.2018.06.008zbMath1403.62192OpenAlexW2883706670MaRDI QIDQ1799645

Nathan Lally, Brian M. Hartman

Publication date: 19 October 2018

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

Full work available at URL: https://doi.org/10.1016/j.insmatheco.2018.06.008

zbMATH Keywords

spatial statisticsGaussian process regressionclaims trianglesinput warpingloss reserves

Mathematics Subject Classification ID

Related Items (3)

Stochastic multi-fidelity surrogate modeling of dendritic crystal growth ⋮ Hierarchical Bayesian Gaussian process regression model for loss reserving using combinations of squared exponential kernels ⋮ A GAMMA MOVING AVERAGE PROCESS FOR MODELLING DEPENDENCE ACROSS DEVELOPMENT YEARS IN RUN-OFF TRIANGLES

Uses Software

• Stan
• NUTS

Cites Work

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• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• Interpolation of spatial data. Some theory for kriging
• Claims reserving: A correlated Bayesian model
• Model Uncertainty in Claims Reserving within Tweedie's Compound Poisson Models
• The Geometry of Random Fields
• A Bayesian Log-Normal Model for Multivariate Loss Reserving
• A NON-PARAMETRIC METHOD FOR INCURRED BUT NOT REPORTED CLAIM RESERVE ESTIMATION

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