Angus S. Macdonald, Stephen J. Richards, Iain D. Currie
Modelling Mortality with Actuarial Applications
Angus S. Macdonald, Stephen J. Richards, Iain D. Currie
Modelling Mortality with Actuarial Applications
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Modern mortality modelling for actuaries and actuarial students, with example R code, to unlock the potential of individual data.
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Modern mortality modelling for actuaries and actuarial students, with example R code, to unlock the potential of individual data.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 386
- Erscheinungstermin: 8. März 2019
- Englisch
- Abmessung: 235mm x 157mm x 25mm
- Gewicht: 710g
- ISBN-13: 9781107045415
- ISBN-10: 110704541X
- Artikelnr.: 50022747
- Verlag: Cambridge University Press
- Seitenzahl: 386
- Erscheinungstermin: 8. März 2019
- Englisch
- Abmessung: 235mm x 157mm x 25mm
- Gewicht: 710g
- ISBN-13: 9781107045415
- ISBN-10: 110704541X
- Artikelnr.: 50022747
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.
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.
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.
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.
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.