04409nam a22005535i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001600186072002300202082001400225100003100239245013300270264004600403300003600449336002600485337002600511338003600537347002400573490005100597505014200648520240200790650001603192650002503208650002703233650002803260650002903288650001903317650001803336650001603354650005003370650004903420650001403469650002603483650001803509650002903527710003403556773002003590776003603610830005103646856004803697912001403745999001903759952007703778978-0-387-75839-8DE-He21320180115171418.0cr nn 008mamaa100301s2008 xxu| s |||| 0|eng d a97803877583989978-0-387-75839-87 a10.1007/978-0-387-75839-82doi 4aQA276-280 7aUFM2bicssc 7aCOM0770002bisacsh04a519.52231 aIacus, Stefano M.eauthor.10aSimulation and Inference for Stochastic Differential Equationsh[electronic resource] :bWith R Examples /cby Stefano M. Iacus. 1aNew York, NY :bSpringer New York,c2008. aXVIII, 286 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aSpringer Series in Statistics,x0172-7397 ;v10 aStochastic Processes and Stochastic Differential Equations -- Numerical Methods for SDE -- Parametric Estimation -- Miscellaneous Topics. aThis 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. Stefano M. Iacus is associate professor of Probability and Mathematical Statistics at the University of Milan, Department of Economics, Business and Statistics. He has a PhD in Statistics at Padua University, Italy and in Mathematics at UniversiteĢ du Maine, France. He is a member of the R Core team for the development of the R statistical environment, Data Base manager for the Current Index to Statistics, and IMS Group Manager for the Institute of Mathematical Statistics. He has been associate editor of the Journal of Statistical Software. 0aStatistics. 0aComputer simulation. 0aMathematical analysis. 0aAnalysis (Mathematics). 0aEconomics, Mathematical. 0aProbabilities. 0aEconometrics.14aStatistics.24aStatistics and Computing/Statistics Programs.24aProbability Theory and Stochastic Processes.24aAnalysis.24aQuantitative Finance.24aEconometrics.24aSimulation and Modeling.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387758381 0aSpringer Series in Statistics,x0172-7397 ;v140uhttp://dx.doi.org/10.1007/978-0-387-75839-8 aZDB-2-SMA c369688d369688 001040708EBookaelibbelibd2018-01-15r2018-01-15w2018-01-15yEBOOK