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 12  Unsupervised Learning
Authors
Louise Francis  Francis Analytics and Actuarial Data Mining, Inc
louise.francis@datamines.com
Chapter Preview
The focus of this chapter will be on various methods of unsupervised learning. Unsupervised learning will be contrasted with supervised learning and the role of unsupervised learning in a supervised analysis will also be discussed. The concept of dimension reduction will first be presented. We will then introduce the common methods of dimension reduction, principal components/factor analysis and clustering. More recent developments on the classic techniques such fuzzy clustering will also be introduced. Illustrative examples will be presented that use publicly available databases. At the end of the chapter we will provide exercises that use data supplied with the chapter.
Data  R Code 
Indices  
Indices 2  
SimClus_Fraud1  
SimClus_Ques.claimsData  
SimClusBI_out1  
Cluster Code for Book  
PrinComp R Code 