This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.
This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
1. Predictive modeling in actuarial science Edward W. Frees and Richard A. Derrig Part I. Predictive Modeling Foundations: 2. Overview of linear models Marjorie Rosenberg 3. Regression with categorical dependent variables Montserrat Guillen 4. Regression with count-dependent variables Jean-Philippe Boucher 5. Generalized linear models Curtis Gary Dean 6. Frequency and severity models Edward W. Frees Part II. Predictive Modeling Methods: 7. Longitudinal and panel data models Edward W. Frees 8. Linear mixed models Katrien Antonio and Yanwei Zhang 9. Credibility and regression modeling Vytaras Brazauskas, Harald Dornheim and Ponmalar Ratnam 10. Fat-tailed regression models Peng Shi 11. Spatial modeling Eike Brechmann and Claudia Czado 12. Unsupervised learning Louise Francis Part III. Bayesian and Mixed Modeling: 13. Bayesian computational methods Brian Hartman 14. Bayesian regression models Luis Nieto-Barajas and Enrique de Alba 15. Generalized additive models and nonparametric regression Patrick L. Brockett, Shuo-Li Chuang and Utai Pitaktong 16. Non-linear mixed models Katrien Antonio and Yanwei Zhang Part IV. Longitudinal Modeling: 17. Time series analysis Piet de Jong 18. Claims triangles/loss reserves Greg Taylor 19. Survival models Jim Robinson 20. Transition modeling Bruce Jones and Weijia Wu.
1. Predictive modeling in actuarial science Edward W. Frees and Richard A. Derrig Part I. Predictive Modeling Foundations: 2. Overview of linear models Marjorie Rosenberg 3. Regression with categorical dependent variables Montserrat Guillen 4. Regression with count-dependent variables Jean-Philippe Boucher 5. Generalized linear models Curtis Gary Dean 6. Frequency and severity models Edward W. Frees Part II. Predictive Modeling Methods: 7. Longitudinal and panel data models Edward W. Frees 8. Linear mixed models Katrien Antonio and Yanwei Zhang 9. Credibility and regression modeling Vytaras Brazauskas, Harald Dornheim and Ponmalar Ratnam 10. Fat-tailed regression models Peng Shi 11. Spatial modeling Eike Brechmann and Claudia Czado 12. Unsupervised learning Louise Francis Part III. Bayesian and Mixed Modeling: 13. Bayesian computational methods Brian Hartman 14. Bayesian regression models Luis Nieto-Barajas and Enrique de Alba 15. Generalized additive models and nonparametric regression Patrick L. Brockett, Shuo-Li Chuang and Utai Pitaktong 16. Non-linear mixed models Katrien Antonio and Yanwei Zhang Part IV. Longitudinal Modeling: 17. Time series analysis Piet de Jong 18. Claims triangles/loss reserves Greg Taylor 19. Survival models Jim Robinson 20. Transition modeling Bruce Jones and Weijia Wu.
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