Mathematics

An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides graduate students and finance professionals with a foundation in nonparametric function estimation and the underlying mathematics. Combining practical applications, mathematically rigorous presentation, and statistical data analysis into a single volume, this book presents detailed instruction in discrete chapters that allow readers to dip in as needed without reading from beginning to end. Coverage includes statistical finance, risk management, portfolio management, and securities pricing to provide a practical knowledge base, and the introductory chapter introduces basic finance concepts for readers with a strictly mathematical background. Economic significance is emphasized over statistical significance throughout, and R code is provided to help readers reproduce the research, computations, and figures being discussed. Strong graphical content clarifies the methods and demonstrates essential visualization techniques, while deep mathematical and statistical insight backs up practical applications. Written for the leading edge of finance, Nonparametric Finance: • Introduces basic statistical finance concepts, including univariate and multivariate data analysis, time series analysis, and prediction • Provides risk management guidance through volatility prediction, quantiles, and value-at-risk • Examines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more • Discusses fundamental theorems of asset pricing, Black-Scholes pricing and hedging, quadratic pricing and hedging, option portfolios, interest rate derivatives, and other asset pricing principles • Provides supplementary R code and numerous graphics to reinforce complex content Nonparametric function estimation has received little attention in the context of risk management and option pricing, despite its useful applications and benefits. This book provides the essential background and practical knowledge needed to take full advantage of these little-used methods, and turn them into real-world advantage. Jussi Klemel?, PhD, is Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of Smoothing of Multivariate Data: Density Estimation and Visualization and Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance.

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. «. . . a goldmine of knowledge on accelerated life testing principles and practices . . . one of the very few capable of advancing the science of reliability. It definitely belongs in every bookshelf on engineering.» –Dev G. Raheja, Quality and Reliability Engineering International «. . . an impressive book. The width and number of topics covered, the practical data sets included, the obvious knowledge and understanding of the author and the extent of published materials reviewed combine to ensure that this will be a book used frequently.» –Journal of the Royal Statistical Society A benchmark text in the field, Accelerated Testing: Statistical Models, Test Plans, and Data Analysis offers engineers, scientists, and statisticians a reliable resource on the effective use of accelerated life testing to measure and improve product reliability. From simple data plots to advanced computer programs, the text features a wealth of practical applications and a clear, readable style that makes even complicated physical and statistical concepts uniquely accessible. A detailed index adds to its value as a reference source.

Written for the professional statistician or graduate statistics student, the primary objective of this book is to describe a system, based on the LISP language, for statistical computing and dynamic graphics to show how it can be used as an effective platform for a wide range of statistical computing tasks ranging from basic calculations to customizing dynamic graphs. In addition, it introduces object-oriented programming and graphics programming in a statistical context. The discussion of these ideas is based on the Lisp-Stat system; readers with access to such a system can reproduce the examples presented and use them as a basis for further experimentation and study.

Kids and teachers can build their own science projects based on exhibits from San Francisco's premiere science museum This revised and updated edition offers instructions for building junior versions, or «snacks,» of the famed Exploratorium's exhibits. The snacks, designed by science teachers, can be used as demonstrations, labs, or as student science projects and all 100 projects are easy to build from common materials. The Exploratorium, a renowned hands-on science museum founded by physicist and educator Frank Oppenheimer, is noted for its interactive exhibits that richly illustrate scientific concepts and stimulate learning. Offers a step-by-step guide for building dynamic science projects and exhibits Includes tips for creating projects made from easy-to-assembly items Thoroughly revised and updated, including new «snacks,» images, and references

An invaluable resource for any college and university striving to meet the National Cost Commission's call to make what colleges do and what it costs more 'transparent' to the public. –Jacqueline E. King, director, federal policy analysis, American Council on Education Defining and measuring faculty productivity are among the most central issues for quality and accountability in higher education. Known for assembling some of the most authoritative research on faculty productivity–and for analyzing its impact on academic and institutional accountability–Michael F. Middaugh presents this comprehensive volume to help campus professionals build greater accountability for students, parents, foundations, governmental organizations, and other concerned constituents. Middaugh first draws from a research study funded by TIAA-CREF's Cooperative Research Grant Program and the Fund for Postsecondary Education within the U.S. Department of Education. He then provides a new framework for analyzing faculty efficiency and emphasizes how the results of faculty work can become the best indicators of productivity. He also applies the joint study findings to the task of developing benchmarks for faculty productivity. Practitioners from any type of campus will find a rich array of data, valuable recommendations, and relevant examples.