Gamma mixture density networks and their application to modelling insurance claim amounts
From MaRDI portal
Publication:2665857
DOI10.1016/j.insmatheco.2021.08.003zbMath1475.91294OpenAlexW3194015155MaRDI QIDQ2665857
Mario V. Wüthrich, Łukasz Delong, Mathias Lindholm
Publication date: 19 November 2021
Published in: Insurance Mathematics \& Economics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.insmatheco.2021.08.003
neural networksmixtures of distributionsregression modelsexpectation-maximization algorithmrate-making
Related Items (2)
Deep quantile and deep composite triplet regression ⋮ Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Gamma shape mixtures for heavy-tailed distributions
- On modeling left-truncated loss data using mixtures of distributions
- LRMoE
- Fitting the Erlang mixture model to data via a GEM-CMM algorithm
- A mixture likelihood approach for generalized linear models
- A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking
- A class of mixture of experts models for general insurance: theoretical developments
- Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models
- Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering
- Modeling loss data using mixtures of distributions
- The EM Algorithm and Extensions, 2E
- FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM
- EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION
- A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES
- A GENERALISED PROPERTY EXPOSURE RATING FRAMEWORK THAT INCORPORATES SCALE-INDEPENDENT LOSSES AND MAXIMUM POSSIBLE LOSS UNCERTAINTY
- AN EM ALGORITHM FOR FITTING A NEW CLASS OF MIXED EXPONENTIAL REGRESSION MODELS WITH VARYING DISPERSION
This page was built for publication: Gamma mixture density networks and their application to modelling insurance claim amounts
