文档介绍：JSS Journal of Statistical Software
July 2008, Volume 26, Book Review 2. /
Reviewer: Tyler McMillen
California State University at Fullerton
Simulation and Inference for Stochastic Differential Equations: With R Ex-
amples
Stefano M. Iacus
Springer-Verlag, New York, 2008.
ISBN 978-0-387-75838. 286 pp. USD .
Introduction
The aim of this book is to develop a unified framework for simulating stochastic differential
equations (SDEs) and estimating parameters of an underlying model from a given time series.
This aim is mostly achieved. The book considers one-dimensional SDEs of the form dX/dt =
b (t, X) + “noise,” or
dX = b(t, X) dt + σ(t, X) dW, (1)
where W (t) is the Wiener process. Here b(t, X) is the drift and σ2(t, X) is the variance of
the process. Such equations have been essfully used to model an enormous variety of
problems in fields as diverse as biology, physics, neuroscience and finance. Most of the exam-
ples treated in this volume are drawn from finance. Although only one-dimensional processes
are considered, as the author notes, most of the simulation techniques that can be used with
one-dimensional processes apply equally well to their multi-dimensional counterparts, and
references are provided for multi-dimensional SDE simulations.
The emphasis is on the practical implementation of code written in R. An R package sde, avail-
able for downl