Regression Modeling with Actuarial and Financial Applications
Published on 30. November 2009
Book
Hardback
584 pages
978-0-521-76011-9 (ISBN)
Description
This text gives budding actuaries and financial analysts a foundation in multiple regression and time series. They 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.
Reviews / Votes
'It would be an ideal text for a semester - or a year-long course in applied statistical methods for actuarial science majors. But it would also be a welcome addition to the bookshelf of pracitcing 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 analysis as a basis for financial decision making ... Perhaps my favorite part of Fee'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 StatisticianMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Professional and scholarly
Illustrations
Worked examples or Exercises; 142 Tables, unspecified; 139 Line drawings, unspecified
Dimensions
Height: 250 mm
Width: 175 mm
Thickness: 36 mm
Weight
1174 gr
ISBN-13
978-0-521-76011-9 (9780521760119)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Edward W. Frees
Regression Modeling with Actuarial and Financial Applications
E-Book
02/2010
1st Edition
Cambridge University Press
€57.49
Available for download
Edward W. Frees
Regression Modeling with Actuarial and Financial Applications
Book
11/2009
Cambridge University Press
€90.80
Shipment within 15-20 days
Edward W. Frees
Regression Modeling with Actuarial and Financial Applications
E-Book
11/2009
Cambridge University Press
€47.99
Available for download
Person
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.
Content
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.