Simulation and Inference for Stochastic Differential Equations : With R Examples

Stefano M. Iacus

ISBN 10: 1441926070 / ISBN 13: 9781441926074

Edité par Springer, Springer, 2010

A propos de cet article

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.

N° de réf. du vendeur 9781441926074

Détails bibliographiques

• Titre: Simulation and Inference for Stochastic Differential Equations : With R Examples
• Éditeur: Springer, Springer
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 1441926070
• ISBN à 13 chiffres: 9781441926074
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 17 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 17 millimètres de profondeur
• Poids: 464 grammes

A propos de ce titre

Synopsis

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.

Présentation de l'éditeur

This book covers a highly relevant topic that is of wide interest, especially in finance, engineering and computational biology. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners with minimal mathematical background.