A New Class of Severity Regression Models with an Application to IBNR Prediction

DOI10.1080/10920277.2020.1729813zbMath1475.91299OpenAlexW3016681904MaRDI QIDQ5165010

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

Publication date: 15 November 2021

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

Full work available at URL: https://doi.org/10.1080/10920277.2020.1729813

zbMATH Keywords

IBNR predictionseverity regression model

Mathematics Subject Classification ID

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

Related Items

JOINT MODELING OF CLAIM FREQUENCIES AND BEHAVIORAL SIGNALS IN MOTOR INSURANCE ⋮ PHASE-TYPE DISTRIBUTIONS FOR CLAIM SEVERITY REGRESSION MODELING ⋮ Mixture Composite Regression Models with Multi-type Feature Selection ⋮ Deep quantile and deep composite triplet regression ⋮ Phase-type mixture-of-experts regression for loss severities ⋮ THE IMPACTS OF INDIVIDUAL INFORMATION ON LOSS RESERVING ⋮ Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models

Uses Software

• LRMoE

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On modeling left-truncated loss data using mixtures of distributions
• Random-Effects Models for Longitudinal Data
• A marked Cox model for the number of IBNR claims: theory
• 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
• A class of mixture of experts models for general insurance: theoretical developments
• Modeling loss data using composite models
• A new class of models for heavy tailed distributions in finance and insurance risk
• Modeling loss data using mixtures of distributions
• Composite Lognormal–Pareto model with random threshold
• Maximum likelihood estimation via the ECM algorithm: A general framework
• FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM
• Modeling claims data with composite Stoppa models
• Micro-level stochastic loss reserving for general insurance
• Machine learning in individual claims reserving
• Fat-Tailed Regression Modeling with Spliced Distributions
• 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
• Modeling with Weibull-Pareto Models
• Extending composite loss models using a general framework of advanced computational tools
• Hierarchical Insurance Claims Modeling
• Identifiability of Finite Mixtures