A seemingly unrelated regression model in a credibility framework.

DOI10.1016/j.insmatheco.2003.09.012zbMath1043.62091OpenAlexW2167070405MaRDI QIDQ1430670

Georgios Pitselis

Publication date: 27 May 2004

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

Full work available at URL: https://doi.org/10.1016/j.insmatheco.2003.09.012

zbMATH Keywords

Empirical linear Bayes estimatorRandom coefficientsSeemingly unrelated regressions

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items

Optimal credibility estimation of random parameters in hierarchical random effect linear model ⋮ Credibility Estimation of Distribution Functions with Applications to Experience Rating in General Insurance ⋮ Robust-efficient credibility models with heavy-tailed claims: a mixed linear models perspective

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Estimating the dimension of a model
• Estimation of seemingly unrelated regression equations
• A longitudinal data analysis interpretation of credibility models
• Credibility theory and the Kalman filter
• Statistical predictor identification
• Finite credibility formulae in evolutionary models
• The linear growth credibility model
• Hierarchical credibility: analysis of a random effect linear model with nested classification
• A hierarchical credibility regression model
• A multi-level hierarchical credibility regression model
• Empirical Bayes credibility
• Recursive credibility estimation
• Empirical linear Bayesian decision rules for a sequence of linear models with different eegressor matrices
• Estimation of Seemingly Unrelated Regressions with Random Coefficients
• Two classes of covariance matrices giving simple linear forecasts
• The Empirical Bayes Approach to Statistical Decision Problems
• Some Comments on C P
• Multivariate Credibility for Aggregate Loss Models
• Case Studies Using Panel Data Models
• An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias