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Simulation and Inference for Stochastic Differential Equations: With R Examples (Springer...
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by Stefano M. Iacus
Sales Rank: 253529
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List Price: $79.95
$63.96
At Amazon
on 10-13-2008
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Hardcover: 286 pages
Publisher: Springer; 1 edition May 5, 2008
Language: English
ISBN-10: 0387758380
ISBN-13: 978-0387758381
Product Dimensions:
9.4 x 6.2 x 0.8 inches
Shipping Weight: 1.2 pounds
Product Review
From the Reviews:
"Overall, this is a good book that fills in several gaps. In addition to collecting and summarizing an enormous quantity of theory, it introduces some novel techniques for inference. Statisticians and mathemeticians who work with time series should find a place on their shelves for this book." (Journal of Statistical Software - Book Reviews)
Product Description
This book is unique because of its focus on the practical implementation of the simulation and estimation methods presented. The book will be useful to practitioners and students with only a minimal mathematical background because of the many R programs, and to more mathematically-educated practitioners.
Many of the methods presented in the book have not been used much in practice because the lack of an implementation in a unified framework. This book fills the gap.
With the R code included in this book, a lot of useful methods become easy to use for practitioners and students. An R package called "sde" provides functions with easy interfaces ready to be used on empirical data from real life applications. Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations.
The book is organized into four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other publications. The third one focuses on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments, and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader who is not an expert in the R language will find a concise introduction to this environment focused on the subject of the book. A documentation page is available at the end of the book for each R function presented in the book.
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Simulation and Inference for Stochastic Differential Equations: With R Examples (Springer...
Available from Amazon
Price: $63.96
Updated on 10-13-2008
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