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
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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 4 - Regression With Count Dependent Variables
Authors
Jean-Philippe Boucher | Université du Québec à Montréal (UQAM)
boucher.jean-philippe@uqam.ca
Chapter Preview
This chapter presents regression models where the random variable is a count and compares different risk classification models for the annual number of claims reported to the insurer. Count regression analysis allows identification of risk factors and prediction of the expected frequency given characteristics of the risk. This chapter details some of the most popular models for the annual number of claims reported to the insurer, the way the actuary should use these models for inference and the way the models should be compared.
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