Regression Modeling with Actuarial and Financial Applications
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- Author: Edward W. Frees, University of Wisconsin, Madison
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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: http://research.bus.wisc.edu/RegActuaries.Read more
- Provides a link between data analysis and data modelling - explains the role of a model
- Easy for a first time instructor - many real world data sets included, and a technical appendix provides the instructor with a great deal of flexibility for course coverage
- Contains many references to different sub-disciplines within the broad field of actuarial science, risk management and finance
Reviews & endorsements
"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 ReviewSee more reviews
"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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- Date Published: February 2010
- format: Adobe eBook Reader
- isbn: 9780511669187
- contains: 139 b/w illus. 142 tables 89 exercises
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
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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Instructors have used or reviewed this title for the following courses
- ACSC (Actuarial Science)
- Advanced Life & Health Insurance
- Applied Regression and Time Series
- Econometrics 1 and 2
- Financial Markets and Institutions
- Financial Research and Modeling
- Probability and statistics application 2
- Regression Analysis
- Regression Modeling in Finance
- Regression and Time Series
- Stat Modeling/Data Analysis ll
- Time Series
- Topics In Statistics: Regression Analysis
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