Machine learning with high-cardinality categorical features in actuarial applications

DOI10.1017/asb.2024.7zbMATH Open1546.91216MaRDI QIDQ6556598

Melantha Wang, Benjamin Avanzi, Greg Taylor, Bernard Wong

Publication date: 17 June 2024

Published in: ASTIN Bulletin (Search for Journal in Brave)

zbMATH Keywords

neural networksrandom effectsvariational inferencegeneralised linear mixed modelsinsurance analyticscategorical featurescategorical embedding

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Actuarial mathematics (91G05)

Cites Work

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• Actuarial statistics with generalized linear mixed models
• Multivariate adaptive regression splines
• Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
• Stochastic loss reserving with mixture density neural networks
• Gamma mixture density networks and their application to modelling insurance claim amounts
• Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods
• Mixed-effects random forest for clustered data
• Effective Statistical Learning Methods for Actuaries I
• Strictly Proper Scoring Rules, Prediction, and Estimation
• On Information and Sufficiency
• Neural Networks for Clustered and Longitudinal Data Using Mixed Effects Models
• The Elements of Statistical Learning
• Statistical Foundations of Actuarial Learning and its Applications

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