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 tothe fundamentals, this book describes several advanced statistical topics that are particularly relevant to actuarial and financial practice, including theanalysis 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. INDICE: 1. Regression and the normal distribution; Part I. Linear Regression: 2. Basic linear regression; 3. Multiple linear regression - I; 4. Multiplelinear 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. Reportwriting: communicating data analysis results; 21. Designing effective graphs;Appendix 1: basic statistical inference; Appendix 2: matrix algebra; Appendix3: probability tables
- ISBN: 978-0-521-76011-9
- Editorial: Cambridge University
- Encuadernacion: Cartoné
- Páginas: 584
- Fecha Publicación: 01/03/2010
- Nº Volúmenes: 1
- Idioma: Inglés