Simulation and Inference for Stochastic Differential Equations : With R Examples

Stefano M. Iacus

ISBN 10: 1441926070 / ISBN 13: 9781441926074

Verlag: Springer, Springer, 2010

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - Stochastic di erential equations model stochastic evolution as time evolves. These models have a variety of applications in many disciplines and emerge naturally in the study of many phenomena. Examples of these applications are physics (see, e. g. , [176] for a review), astronomy [202], mechanics [147], economics [26], mathematical nance [115], geology [69], genetic analysis (see, e. g. , [110], [132], and [155]), ecology [111], cognitive psychology (see, e. g. , [102], and [221]), neurology [109], biology [194], biomedical sciences [20], epidemi- ogy [17], political analysis and social processes [55], and many other elds of science and engineering. Although stochastic di erential equations are quite popular models in the above-mentioned disciplines, there is a lot of mathem- ics behind them that is usually not trivial and for which details are not known to practitioners or experts of other elds. In order to make this book useful to a wider audience, we decided to keep the mathematical level of the book su ciently low and often rely on heuristic arguments to stress the underlying ideas of the concepts introduced rather than insist on technical details. Ma- ematically oriented readers may nd this approach inconvenient, but detailed references are always given in the text. As the title of the book mentions, the aim of the book is twofold.

Bestandsnummer des Verkäufers 9781441926074

Bibliografische Details

• Titel: Simulation and Inference for Stochastic Differential Equations : With R Examples
• Verlag: Springer, Springer
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 1441926070
• ISBN-13: 9781441926074
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 155 H x 17 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 17 mm
• Gewicht: 464 Gramm

Über diesen Titel

Inhaltsangabe

This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers. While there are several recent texts available that cover stochastic differential equations, the concentration here on inference makes this book stand out. No other direct competitors are known to date. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners and students with minimal mathematical background. What’s more, because of the many R programs, the information here is appropriate for many mathematically well educated practitioners, too. Many of the methods presented in the book have, so far, not been used much in practice because of the lack of an implementation in a unified framework. Iacus’ book bridges this gap. With the R code included, a lot of useful methods become easy to use.

Über die Autorin bzw. den Autor

Stefano M. Iacus, PhD, is full professor of statistics in the Department of Economics, Management and Quantitative Methods at the University of Milan. He has been a member of the R Core Team (1999-2014) for the development of the R statistical environment and is now a member of the R Foundation. His research interests include inference for stochastic processes, simulation, computational statistics, causal inference, text mining, and sentiment analysis. Nakahiro Yoshida, PhD, is full professor at the Graduate School of Mathematical Sciences, University of Tokyo. He is working in theoretical statistics, probability theory, computational statistics, and financial data analysis. He was awarded the Japan Statistical Society Award in 2009 and the Analysis Prize from the Mathematical Society of Japan in 2006.