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 10  FatTailed Regression Models
Authors
Peng Shi  University of WisconsinMadison
pshi@bus.wisc.edu
Chapter Preview
In the actuarial context, fattailed phenomena are often observed where the probability of extreme events is higher than that implied by the normal distribution. The traditional regression, emphasizing the center of the distribution, might not be appropriate when dealing with data with fattailed properties. Overlooking the extreme values in the tail could lead to biased inference for ratemaking and valuation. In response, this chapter discusses four fattailed regression techniques that fully utilize the information from the entire distribution: transformation, models based on the exponential family and on generalized distributions, and median regression.
Data  R Code 
Claim  
Officeout  
Section 3  
Section 4.3(SAS Code)  
Section 5 