Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models

DOI10.1080/10920277.2021.2013896OpenAlexW4220949333MaRDI QIDQ5877347

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

Publication date: 10 February 2023

Published in: North American Actuarial Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10920277.2021.2013896

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Actuarial mathematics (91G05)

Related Items (3)

Mixture Composite Regression Models with Multi-type Feature Selection ⋮ Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models ⋮ Soft splicing model: bridging the gap between composite model and finite mixture model

Cites Work

• Unnamed Item
• On modeling left-truncated loss data using mixtures of distributions
• Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data
• A marked Cox model for the number of IBNR claims: theory
• Mixture of linear experts model for censored data: a novel approach with scale-mixture of normal distributions
• A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance
• Fitting the Erlang mixture model to data via a GEM-CMM algorithm
• Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions
• Modeling the number of hidden events subject to observation delay
• EM algorithms for multivariate Gaussian mixture models with truncated and censored data
• A class of mixture of experts models for general insurance: theoretical developments
• Multivariate mixtures of Erlangs for density estimation under censoring
• A dependent frequency-severity approach to modeling longitudinal insurance claims
• Modeling loss data using mixtures of distributions
• Finite mixture models
• FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM
• Micro-level stochastic loss reserving for general insurance
• A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES
• A MARKED COX MODEL FOR THE NUMBER OF IBNR CLAIMS: ESTIMATION AND APPLICATION
• THE IMPACTS OF INDIVIDUAL INFORMATION ON LOSS RESERVING
• A New Class of Severity Regression Models with an Application to IBNR Prediction
• General Insurance Deductible Ratemaking
• Hierarchical Insurance Claims Modeling

This page was built for publication: Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models