A class of mixture of experts models for general insurance: theoretical developments

DOI10.1016/j.insmatheco.2019.09.007zbMath1427.91228OpenAlexW2977986876MaRDI QIDQ2010898

X. Sheldon Lin, Andrei L. Badescu, T. C. Fung

Publication date: 28 November 2019

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

Full work available at URL: https://doi.org/10.1016/j.insmatheco.2019.09.007

zbMATH Keywords

neural networkclaim frequency and severity modelingdenseness theorymixture of experts modelsmultivariate regression analysis

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Actuarial mathematics (91G05)

Related Items

Gamma mixture density networks and their application to modelling insurance claim amounts, The multivariate mixed negative binomial regression model with an application to insurance a posteriori ratemaking, Bivariate Mixed Poisson Regression Models with Varying Dispersion, Frequency-severity experience rating based on latent Markovian risk profiles, Soft splicing model: bridging the gap between composite model and finite mixture model, Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models, Robust claim frequency modeling through phase-type mixture-of-experts regression, Phase-type mixture-of-experts regression for loss severities, Fitting multivariate Erlang mixtures to data: a roughness penalty approach, A New Class of Severity Regression Models with an Application to IBNR Prediction, Two-step risk analysis in insurance ratemaking, A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES, Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models

Uses Software

• flexmix
• EMpht

Cites Work

• Multivariate negative binomial models for insurance claim counts
• A mixture of experts model for rank data with applications in election studies
• A priori ratemaking using bivariate Poisson regression models
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• A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES
• Multivariate Cox Hidden Markov models with an application to operational risk
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