Teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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.
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.
Rezensionen
'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 Statistician
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