Predictive modeling techniques

著者

書誌事項

Predictive modeling techniques

edited by Edward W. Frees, Richard A. Derrig, Glenn Meyers

（International series on actuarial science, Predictive modeling applications in actuarial science ; v. 1）

Cambridge University Press, 2014

大学図書館所蔵 件 / 全7件

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.

目次

  • 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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