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This book describes a novel method for mortality modeling applying the shifting feature of the mortality curve. In Japan, the increase and pace of the extension in life expectancy have been quite remarkable. Therefore, existing mortality models often cannot capture the peculiarities of Japanese mortality, nor can the LeeCarter model, which is now regarded internationally as a standard model. One of the important concepts to model recent Japanese mortality is a shifting feature. In this book, the linear difference model, which has many advantages for modeling and analyzing Japanese mortality, is introduced. The book shows applications of the model to mortality projection with a tangent vector field approach and decomposition of the change of modal age at death. The models introduced here are useful tools for modeling mortality with strong shifting features, as in Japan

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

In Western countries of the Northern hemisphere, mortality is typically larger in winter than in summer which is attributed to the detrimental effects of cold to health. This book investigates whether sociodemographic and socioeconomic factors play a role as important for seasonal mortality as they do for mortality in general