Effective Statistical Learning Methods for Actuaries I

DOI10.1007/978-3-030-25820-7zbMath1426.62003OpenAlexW4233610599MaRDI QIDQ5233523

Julien Trufin, Donatien Hainaut, Michel M. Denuit

Publication date: 11 September 2019

Published in: Springer Actuarial (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-25820-7

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05) Generalized linear models (logistic models) (62J12) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01) Learning and adaptive systems in artificial intelligence (68T05) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to game theory, economics, and finance (91-01) Actuarial mathematics (91G05)

Related Items (11)

Generalization error for Tweedie models: decomposition and error reduction with bagging ⋮ JOINT MODELING OF CLAIM FREQUENCIES AND BEHAVIORAL SIGNALS IN MOTOR INSURANCE ⋮ The added value of dynamically updating motor insurance prices with telematics collected driving behavior data ⋮ The GLM framework of the Lee–Carter model: a multi-country study ⋮ One-step closed-form estimator for generalized linear model with categorical explanatory variables ⋮ Bayesian CART models for insurance claims frequency ⋮ Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link ⋮ Insurance pricing with hierarchically structured data an illustration with a workers' compensation insurance portfolio ⋮ WAVELET-BASED FEATURE EXTRACTION FOR MORTALITY PROJECTION ⋮ Fitting Nonstationary Cox Processes: An Application to Fire Insurance Data ⋮ An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests

Uses Software

• R

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