Actuarial Statistics with R: Theory and Case Studies

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.