Closed-form maximum likelihood estimator for generalized linear models in the case of categorical explanatory variables: application to insurance loss modeling

DOI10.1007/s00180-019-00918-7zbMath1482.62005OpenAlexW2913178339MaRDI QIDQ782645

Christophe Dutang, Tom Rohmer, Alexandre Brouste

Publication date: 28 July 2020

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://basepub.dauphine.fr/handle/123456789/19679

zbMATH Keywords

heavy-tailed distributionsregression modelsexplicit MLEinsurance claim modeling

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to actuarial sciences and financial mathematics (62P05) Generalized linear models (logistic models) (62J12) Actuarial mathematics (91G05)

Related Items (2)

One-step closed-form estimator for generalized linear model with categorical explanatory variables ⋮ An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests

Uses Software

• R
• MASS (R)
• DLMF
• GAMLSS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Regression with response distributions of Pareto-type
• Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models
• Burr regression and portfolio segmentation
• Bayesian modelling of the time delay between diagnosis and settlement for critical illness insurance using a Burr generalised-linear-type model
• Statistical Analysis of Financial Data in S-Plus
• On the existence of maximum likelihood estimates in logistic regression models
• Non-Life Insurance Pricing with Generalized Linear Models
• Log-Linear Models for Frequency Tables with Ordered Classifications
• Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach
• Statistics of Extremes
• Analytical closed-form solution for binary logit regression by categorical predictors
• Generalized Additive Models for Location, Scale and Shape

This page was built for publication: Closed-form maximum likelihood estimator for generalized linear models in the case of categorical explanatory variables: application to insurance loss modeling