Predictive Modeling Applications in Actuarial Science
- Volume 1
- Introduction
- Predictive Modeling Foundations
- Predictive Modeling Methods
- Bayesian and Mixed Modeling
- Longitudinal Modeling
- Volume 2
- Generalized Linear Model
- Extensions of the Generalized Linear Model
- Unsupervised Predictive Modeling Methods
-
Applications on Current Problems in Actuarial Science
- Chapter 8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- Chapter 9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Chapter 8 - Linear Mixed Models
Authors
Katrien Antonio | University of Amsterdam and KU Leuven
katrien.antonio@econ.kuleuven.be
Yanwei Zhang | University of Southern California
actuaryzhang10@gmail.com
Chapter Preview
We give a general discussion of linear mixed models and continue with illustrating specific actuarial applications of this type of models. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques and methods of inference. We include three worked out examples with the R lme4 package and use ggplot2 for the graphs.
| Data | R Code |
| Counts Workers Data | |
| Credit Dannenburg Data | Credit Dannenburg Code |
| Loss Workers Data | Work Comp Loss Model |
| My Hachemeister Data | Hachemeister Code |
| wcLoss | Workers Freq Model |