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 3  Regression with Categorical Dependent Variables
Authors
Montserrat GuillĂ©n  University of Barcelona
mguillen@ub.edu
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This chapter presents regression models where the dependent variable is categorical, whereas covariates can either be categorical or continuous. In the first part binary dependent variable models are presented and the second part is aimed at covering general categorical dependent variable models, where the dependent variable has more than two outcomes.
This chapter is illustrated with data sets, inspired by reallife situations. The corresponding R programs for estimation are also provided. They are based on R packages glm and mlogit. The same output can be obtained when using SAS or similar software programs for estimating the models presented in this chapter.
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