<p><br/>Simulation and Inference for Stochastic Differential Equations: With R Examples</p><div class="c"></div><div class="tpc_content"><div class="c" id="p_tpc"></div><div class="f14">Simulation and Inference for Stochastic Differential Equations: With R Examples (Springer Series in Statistics)<br/>By Stefano M. Iacus<br/><br/><br/><br/>Publisher:&nbsp;&nbsp; Springer <br/>Number Of Pages:&nbsp;&nbsp; 286 <br/>Publication Date:&nbsp;&nbsp; 2008-05-05 <br/>ISBN-10 / ASIN:&nbsp;&nbsp; 0387758380 <br/>ISBN-13 / EAN:&nbsp;&nbsp; 9780387758381 <br/><br/><br/>Product Description: <br/><br/><br/>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.<br/><br/>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.<br/><br/>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.<br/><br/>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.<br/><br/><br/>Another dowloding link</div><div class="f14"><br/><a id="url_1" href="http://rapidshare.com/files/226973096/0387758380.rar" target="_blank"><font color="#0070af">http://rapidshare.com/files/226973096/0387758380.rar</font></a><br/></div></div><p></p>