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 15 - Generalized Additive Models and Nonparametric Regression
Authors
Patrick L. Brockett | University of Texas at Austin
brockett@mail.utexas.edu
Shuo-Li 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 xj to be an additive but not necessarily a monomial function of the predictor variables xj. 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).
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| lowess | |
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