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 Workersâ€™ Compensation LargeLoss Regression Analysis
 Chapter 10  A Framework for Managing Claim Escalation Using Predictive Modeling
 Chapter 11  Predictive Modeling for UsageBased Auto Insurance
Chapter 4  Regression With Count Dependent Variables
Authors
JeanPhilippe Boucher  Université du Québec à Montréal (UQAM)
boucher.jeanphilippe@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.
Data 
R Code 
Singapore Auto 