Regression Modeling with Actuarial and Financial Applications

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Statistical techniques can be used to address new situations. This is important in a rapidly evolving risk management and financial world. Analysts with a strong statistical background understand that a large data set can represent a treasure trove of information to be mined and can yield a strong competitive advantage. This book provides budding actuaries and financial analysts with a foundation in multiple regression and time series. Readers will learn about these statistical techniques using data on the demand for insurance, lottery sales, foreign exchange rates, and other applications. Although no specific knowledge of risk management or finance is presumed, the approach introduces applications in which statistical techniques can be used to analyze real data of interest. In addition to the fundamentals, this book describes several advanced statistical topics that are particularly relevant to actuarial and financial practice, including the analysis of longitudinal, two-part (frequency/severity), and fat-tailed data. Datasets with detailed descriptions, sample statistical software scripts in "R" and "SAS," and tips on writing a statistical report, including sample projects, can be found on the book’s Web site:
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Editorial Reviews

From the Publisher
"This is an excellent book written by an all-round writer. He is a Fellow of both the Society of Actuaries and the American Statistical Association. Hence, it is not surprising that the book fills the gap between modern statistics and traditional actuarial/risk management methods. Need for this kind of book is obvious. I highly recommend it to any person who wishes to learn how to use statistical methods for actuarial and financial applications.
Lasse Koskinen, International Statistical Review

"The author provides an outstanding list of references at the end of the chapters that provides additional reading on the various topics. In addition the author provides programs in SAS and R as well as output form these packages."
Michael R. Chernick, Significance Magazine

"a welcome addition to the bookshelf of practicing actuaries at all levels, both actuarial students charged with conducting analyses for which the methods discussed in the book are most relevant, and senior managers who use such analyses as a basis for financial decision-making. Perhaps my favorite part of Frees’s book is the final two chapters, on Report Writing and Designing Effective Graphs. If these fine essays do not already appear somewhere on the Society of Actuaries syllabus, they should be added immediately."
Ronald C. NEATH, The American Statistician

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Product Details

  • ISBN-13: 9780521135962
  • Publisher: Cambridge University Press
  • Publication date: 12/31/2009
  • Series: International Series on Actuarial Science Series
  • Edition description: New Edition
  • Pages: 584
  • Sales rank: 1,125,392
  • Product dimensions: 6.80 (w) x 9.70 (h) x 1.30 (d)

Meet the Author

Edward W. (Jed) Frees is a Professor of Business at the University of Wisconsin–Madison and is holder of the Assurant Health Insurance Professorship of Actuarial Science. He is a Fellow of both the Society of Actuaries (SoA) and the American Statistical Association (ASA). Professor Frees is the author of Longitudinal and Panel Data (2004) and has published more than fifty articles in leading refereed academic journals.
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Table of Contents

1. Regression and the normal distribution; Part I. Linear Regression: 2. Basic linear regression; 3. Multiple linear regression – I; 4. Multiple linear regression – II; 5. Variable selection; 6. Interpreting regression results; Part II. Topics in Time Series: 7. Modeling trends; 8. Autocorrelations and autoregressive models; 9. Forecasting and time series models; 10. Longitudinal and panel data models; Part III. Topics in Nonlinear Regression: 11. Categorical dependent variables; 12. Count dependent variables; 13. Generalized linear models; 14. Survival models; 15. Miscellaneous regression topics; Part IV. Actuarial Applications: 16. Frequency-severity models; 17. Fat-tailed regression models; 18. Credibility and bonus-malus; 19. Claims triangles; 20. Report writing: communicating data analysis results; 21. Designing effective graphs; Appendix 1: basic statistical inference; Appendix 2: matrix algebra; Appendix 3: probability tables.
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