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.
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.
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