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 15  Generalized Additive Models and Nonparametric Regression
Authors
Patrick L. Brockett  University of Texas at Austin
brockett@mail.utexas.edu
ShuoLi Chuang  University of Texas at Austin
shuolic@mail.utexas.edu
Utai Pitaktong  University of Texas at Austin
Chapter Preview
Generalized Additive Models (GAMs) provide a further generalization of both linear regression and generalized linear models (GLM) by allowing the relationship between the response variable y and the individual predictor variables x_{j} to be an additive but not necessarily a monomial function of the predictor variables x_{j}. Also, as with the GLM, a nonlinear link function can connect the additive concatenation of the nonlinear functions of the predictors to the mean of the response variable giving flexibility in distribution form as discussed in the GLM chapter. The key factor in creating the GAM is the determination and construction of the functions of the predictor variables (called smoothers). Different methods of fit and functional forms for the smoothers are discussed. The GAM can be considered as more data driven (to determine the smoothers) as opposed to model driven (the additive monomial functional form assumption in linear regression and GLM).
Data  R Code 
Link to Datasets  
gam  
lowess  
Running Average 