Angus S. Macdonald

Modelling Mortality with Actuarial Applications (eBook, PDF)

• Format: PDF

Produktbeschreibung

Actuaries have access to a wealth of individual data in pension and insurance portfolios, but rarely use its full potential. This book will pave the way, from methods using aggregate counts to modern developments in survival analysis. Based on the fundamental concept of the hazard rate, Part I shows how and why to build statistical models, based on data at the level of the individual persons in a pension scheme or life insurance portfolio. Extensive use is made of the R statistics package. Smooth models, including regression and spline models in one and two dimensions, are covered in depth in Part II. Finally, Part III uses multiple-state models to extend survival models beyond the simple life/death setting, and includes a brief introduction to the modern counting process approach. Practising actuaries will find this book indispensable, and students will find it helpful when preparing for their professional examinations.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 3. Mai 2018
• Englisch
• ISBN-13: 9781108686334
• Artikelnr.: 53398374

Autorenporträt

Angus S. Macdonald is Professor of Actuarial Mathematics at Heriot-Watt University, Edinburgh. He is an actuary with much experience of modeling mortality and other life histories, particularly in connection with genetics, and as a member of Continuous Mortality Investigation committees.

Inhaltsangabe

Preface
Part I. Analysing Portfolio Mortality: 1. Introduction
2. Data preparation
3. The basic mathematical model
4. Statistical inference with mortality data
5. Fitting a parametric survival model
6. Model comparison and tests of fit
7. Modelling features of the portfolio
8. Non-parametric methods
9. Regulation
Part II. Regression and Projection Models: 10. Methods of graduation I - regression models
11. Methods of graduation II - smooth models
12. Methods of graduation III - 2-dimensional models
13. Methods of graduation IV - forecasting
Part III. Multiple-State Models: 14. Markov multiple-state models
15. Inference in the Markov model
16. Competing risks models
17. Counting-process models
Appendix A. R commands
Appendix B. Basic likelihood theory
Appendix C. Conversion to published tables
Appendix D. Numerical integration
Appendix E. Mean and variance-covariance of a vector
Appendix F. Differentiation with respect to a vector
Appendix G. Kronecker product of two matrices
Appendix H. R functions and programs
References
Author index
Index.