000 04379nam a22005535i 4500
001 978-0-387-75839-8
003 DE-He213
005 20180115171418.0
007 cr nn 008mamaa
008 100301s2008 xxu| s |||| 0|eng d
020 _a9780387758398
024 7 _a10.1007/978-0-387-75839-8
050 4 _aQA276-280
072 7 _aUFM
072 7 _aCOM077000
082 0 4 _a519.5
100 1 _aIacus, Stefano M.
245 1 0 _aSimulation and Inference for Stochastic Differential Equations
_h[electronic resource] :
_bWith R Examples /
_cby Stefano M. Iacus.
264 1 _aNew York, NY :
_bSpringer New York,
300 _aXVIII, 286 p.
_bonline resource.
336 _atext
337 _acomputer
338 _aonline resource
347 _atext file
490 1 _aSpringer Series in Statistics,
_x0172-7397 ;
505 0 _aStochastic Processes and Stochastic Differential Equations -- Numerical Methods for SDE -- Parametric Estimation -- Miscellaneous Topics.
520 _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 Université 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.
650 0 _aStatistics.
650 0 _aComputer simulation.
650 0 _aMathematical analysis.
650 0 _aAnalysis (Mathematics).
650 0 _aEconomics, Mathematical.
650 0 _aProbabilities.
650 0 _aEconometrics.
650 1 4 _aStatistics.
650 2 4 _aStatistics and Computing/Statistics Programs.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aAnalysis.
650 2 4 _aQuantitative Finance.
650 2 4 _aEconometrics.
650 2 4 _aSimulation and Modeling.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
830 0 _aSpringer Series in Statistics,
_x0172-7397 ;
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-387-75839-8
912 _aZDB-2-SMA
999 _c369688