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Fundamentals Of Actuarial Mathematics

Author: S. David Promislow
Publisher: John Wiley & Sons
ISBN: 1118782496
Size: 73.78 MB
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Provides a comprehensive coverage of both the deterministic and stochastic models of life contingencies, risk theory, credibility theory, multi-state models, and an introduction to modern mathematical finance. New edition restructures the material to fit into modern computational methods and provides several spreadsheet examples throughout. Covers the syllabus for the Institute of Actuaries subject CT5, Contingencies Includes new chapters covering stochastic investments returns, universal life insurance. Elements of option pricing and the Black-Scholes formula will be introduced.

Mathematik Und Technologie

Author: Christiane Rousseau
Publisher: Springer-Verlag
ISBN: 3642300928
Size: 42.63 MB
Format: PDF, ePub, Mobi
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Zusammen mit der Abstraktion ist die Mathematik das entscheidende Werkzeug für technologische Innovationen. Das Buch bietet eine Einführung in zahlreiche Anwendungen der Mathematik auf dem Gebiet der Technologie. Meist werden moderne Anwendungen dargestellt, die heute zum Alltag gehören. Die mathematischen Grundlagen für technologische Anwendungen sind dabei relativ elementar, was die Leistungsstärke der mathematischen Modellbildung und der mathematischen Hilfsmittel beweist. Mit zahlreichen originellen Übungen am Ende eines jeden Kapitels.

Financial Modelling In Python

Author: Shayne Fletcher
Publisher: John Wiley & Sons
ISBN: 0470747897
Size: 54.23 MB
Format: PDF, Mobi
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"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." –David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.

Clinical Trial Design

Author: Guosheng Yin
Publisher: John Wiley & Sons
ISBN: 0470581719
Size: 69.70 MB
Format: PDF, Kindle
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There has been enormous interest and development in Bayesian adaptive designs, especially for early phases of clinical trials. nevertheless, for phase III trials, frequentist methods still play a dominant role through controlling type I and type II errors in the hypothesis testing framework. This book provides an overview of the fundamentals of clinical trials, the key terminologies and concepts, and a brief review and comparison on Bayesian and frequentist estimation and inference procedures. From the practical point of view, this book introduces various statistical methods that are commonly used for designing clinical trials and interim monitoring and analysis. Adaptation has a broad meaning in both Bayesian and frequentist perspectives, such as dose finding, trial early stopping for futility or superiority, dropping or adding an arm, seamless transitions between consecutive phases, group sequential methods, sample size re-estimation, adaptive randomization, and subpopulation enrichment, etc. Comprehensive discussions on a variety of statistical designs, their properties, and operating characteristics for phase I, II, and III clinical trials are provided, as well as an introduction on phase IV trials. Many practical issues and challenges arising in clinical trials are addressed, and while the book mainly focuses on the Bayesian approaches for phase I and II trial designs, many important frequentist methods for phase III clinical trails are included. In addition, advanced and up-to-date topics such as jointly modeling toxicity and efficacy, seamless phase I/II trial designs, multiple testing, causal inference and noncompliance, adaptive randomization, issues associated with delayed outcomes, dose finding with combined drugs, and targeted therapy designs in personalized medicine development are discussed. Chapter coverage includes Fundamentals of Clinical Trials; Frequentist versus Bayesian Statistics; Phase I, II, and III Trial Designs, Adaptive Randomization, Late-onset Toxicity, Drug-combination Trials, and Targeted Therapy Design.