This book is written primarily for actuarial students and practitioners who wish to learn the basic fundamentals and applications of modern statistical methods using R programming. It provides data analytic tools utilizing supervised and unsupervised learning, as well as time series and simulation models. Twelve practical case studies demonstrate applications of topics that include generalized linear models, decision trees, principal component analysis and cluster analysis.
This book covers several topics on data analysis and statistical learning prescribed by the International Actuarial Association (IAA). In particular, it has been designed to cover the learning objectives for the SOA’s Statistics for Risk Modeling (SRM) Exam. Many materials from this book also cover parts of the syllabus for the CAS Modern Actuarial Statistics (MAS-I and MAS-II) Exam. It is broadly intended for students and practitioners to learn R programming and its applications in actuarial science, finance, and quantitative risk management.
This book differs from existing books in several ways. First, it is uniquely prepared as a single source to cover traditional and modern methods of data analytics. Second, it teaches the steps of how to implement and validate models in R at an elementary level. Third, it gives students the opportunity to experience the power of applied statistics and R programming first-hand with real world problems. Finally, it provides a theoretical framework, but at the same time, uses the case study method to better connect theory and practice and bridge the gap between academia and industry.
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Guojun Gan, PhD, FSA, is an Assistant Professor in the Department of Mathematics at the University of Connecticut, Storrs, CT, where he has been since August 2014. He received a BS degree from Jilin University, Changchun, China, in 2001 and MS and PhD degrees from York University, Toronto, Canada, in 2003 and 2007, respectively. His research interests include data mining and actuarial science. He has published several books and research papers on a variety of topics.
Emiliano A. Valdez, PhD, FSA, is a Professor in the Department of Mathematics at the University of Connecticut, Storrs, CT, where he has been since August 2015. His academic experience includes several years of teaching and pursuing research in three different continents: North America, Australia, and Asia. Dr. Valdez has been known for his work on copula models and has been awarded several prizes that include the Edward A. Lew Award, the Halmstad Memorial Prize, and the Hachemeister Prize.
Table of Contents
I Supervised Learning
1 Simple Linear Regression
2 Case Study: Implementing the CAPM
3 Multiple Linear Regression Models
4 Case Study: Predicting Intraday Movements
5 Case Study: Estimating Fair Market Values
6 Generalized Linear Models
7 Case Study: Predicting Demand
8 Case Study: Modeling the Number of Auto Claims
9 Case Study: Modeling the Loss Severity
10 Decision Trees
11 Case Study: Decision Trees
II Unsupervised Learning
12 Data Clustering
13 Case Study: Clustering Variable Annuity Policies
14 Principal Component Analysis
15 Case Study: PCA on Interest Rate Swaps
III Time Series Models
16 Time Series Models
17 Case Study: Forecasting Exchange Rates
18 Case Study: Profitability Analysis
19 Case Study: Simulating the Future Lifetime
Appendix: Introduction to R
Download: Datasets and R code used in the textbook (.zip folder)