Regression modeling with actuarial and financial applications / Edward W. Frees.
By: Frees, Edward W
.
Material type: 
TextSeries: Publisher: Cambridge ; New York : Cambridge University Press, 2010Description: xvii, 565 p. : ill. ; 25 cm.ISBN: 9780521760119; 0521760119; 9780521135962 (pbk.); 0521135966 (pbk.).Subject(s): Insurance -- Statistical methods | Finance -- Statistical methods | Regression analysisDDC classification: 519.536 Online resources: WorldCat details | Ebook Fulltext | Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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Includes bibliographical references and index.
Table of contents 1. Regression and the Normal Distribution --
pt. I. Linear Regression --
2. Basic Linear Regression --
3. Multiple Linear Regression --
I --
4. Multiple Linear Regression --
II --
5. Variable Selection --
6. Interpreting Regression Results --
pt. 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 --
pt. III. Topics in Nonlinear Regression --
11. Categorical Dependent Variables --
12. Count Dependent Variables --
13. Generalized Linear Models --
14. Survival Models --
15. Miscellaneous Regression Topics --
pt. 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.
Summary:
"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 Read more...
Applied Statistics
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