000  04379nam a22005535i 4500  

001  9780387758398  
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005  20180115171418.0  
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008  100301s2008 xxu s  0eng d  
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_a9780387758398 _99780387758398 

024  7 
_a10.1007/9780387758398 _2doi 

050  4  _aQA276280  
072  7 
_aUFM _2bicssc 

072  7 
_aCOM077000 _2bisacsh 

082  0  4 
_a519.5 _223 
100  1 
_aIacus, Stefano M. _eauthor. 

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, _c2008. 

300 
_aXVIII, 286 p. _bonline resource. 

336 
_atext _btxt _2rdacontent 

337 
_acomputer _bc _2rdamedia 

338 
_aonline resource _bcr _2rdacarrier 

347 
_atext file _bPDF _2rda 

490  1 
_aSpringer Series in Statistics, _x01727397 ; _v1 

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 mathematicallyeducated 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 pseudolikelihood 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: _z9780387758381 
830  0 
_aSpringer Series in Statistics, _x01727397 ; _v1 

856  4  0  _uhttp://dx.doi.org/10.1007/9780387758398 
912  _aZDB2SMA  
999 
_c369688 _d369688 