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 14 - Bayesian Regression Models
Authors
Luis Nieto-Barajas | ITAM, Mexico
lnieto@itam.mx
Enrique de Alba | INEGI, Mexico
dealba@itam.mx, edealba@gmail.com
Chapter Preview
In this chapter we approach many of the topics of the previous chapters, but from a Bayesian viewpoint. Initially we cover the foundations of Bayesian inference. We then describe the Bayesian linear and generalized regression models. We concentrate on the regression models with zero-one and count response and illustrate the models with real datasets. We also cover hierarchical prior specifications in the context of mixed models. We finish with a description of a semiparametric linear regression model with a nonparametric specification of the error term. We also illustrate its advantage with respect to the fully parametric setting using a real data set.
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