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 16 - Non-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 start with a discussion of model families for multilevel data outside the Gaussian framework. We continue with Generalized Linear Mixed Models (GLMMs), which enable generalized linear modeling with multilevel data. The Chapter includes highlights of estimation techniques for GLMMs, in the frequentist as well as Bayesian context. We continue with a discussion of Non-Linear Mixed Models (NLMMs). The Chapter concludes with an extensive case study using a selection of R packages for GLMMs.
| Data | R Code |
| CountsWorkers | |
| WorkersFreqData | |
| WorkersFreqModel | |
| WorkersFreqModelVarSlopes | |
| WorkCompFreq | |
| getInits | |
| glmmBUGSExample |