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Modelling Mortality with Actuarial Applications

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Modelling Mortality with Actuarial Applications

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Description

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.

Author Biography:

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. Stephen J. Richards is an actuary and principal of Longevitas Ltd., Edinburgh, a software and consultancy firm that uses many of the models described in this book with life insurance and pension scheme clients worldwide. Iain D. Currie is an Honorary Research Fellow at Heriot-Watt University, Edinburgh. As a statistician, he was chiefly responsible for the development of the spline models described in this book, and their application to actuarial problems.
Release date NZ
May 3rd, 2018
Pages
384
Audience
  • Professional & Vocational
Illustrations
42 Tables, black and white; 2 Halftones, black and white; 93 Line drawings, black and white
Dimensions
158x235x20
ISBN-13
9781107045415
Product ID
27456002

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