LEADER 05217nam 22006015 450 001 9910300127403321 005 20220627192323.0 010 $a3-319-55569-3 024 7 $a10.1007/978-3-319-55569-0 035 $a(CKB)4100000004823266 035 $a(DE-He213)978-3-319-55569-0 035 $a(MiAaPQ)EBC5407664 035 $a(PPN)229490964 035 $a(EXLCZ)994100000004823266 100 $a20180601d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSimulation and Inference for Stochastic Processes with YUIMA$b[electronic resource] $eA Comprehensive R Framework for SDEs and Other Stochastic Processes /$fby Stefano M. Iacus, Nakahiro Yoshida 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XIII, 268 p. 83 illus., 32 illus. in color.) 225 1 $aUse R!,$x2197-5736 311 $a3-319-55567-7 327 $a1 Introduction -- 2 Diffusion processes -- 3 Compound Poisson processes -- 4 Stochastic differential equations driven by Lévy processes -- 5 Stochastic differential equations driven by the fractional Brownian motion -- 6 CARMA models -- 7 COGARCH models -- Reference -- Index. 330 $aThe YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of stochastic processes and then presents both simulation experiments and applications to real data. Although these processes have been originally proposed in physics and more recently in finance, they are becoming popular also in biology due to the fact the time course experimental data are now available. The YUIMA  package, already available on CRAN, can be freely downloaded and this companion book will make the user able to start his or her analysis from the first page. Contains both theory and code with step-by-step examples and figures Uses YUIMA package to implement the latest techniques available in the literature of inference for stochastic processes Shows how to create the description of very abstract models in the same way they are described in theoretical papers but with an extremely easy interface Stefano M. Iacus, PhD, is full professor of statistics the Department of Economics, Management and Quantitative Methods at the University of Milan. He has been a member of the R Core Team (1999-2014) for the development of the R statistical environment and now member of the R Foundation. His research interests include inference for stochastic processes, simulation, computational statistics, causal inference, text mining, and sentiment analysis.  Nakahiro Yoshida, PhD, is a professor at the Graduate School of Mathematical Sciences, University of Tokyo. He is working in theoretical statistics, probability theory, computational statistics, and financial data analysis. He was awarded the Japan Statistical Society Award in 2009 and the Analysis Prize from the Mathematical Society of Japan in 2006. 410 0$aUse R!,$x2197-5736 606 $aStatistics  606 $aMathematical statistics 606 $aProbabilities 606 $aR (Computer program language) 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 615 0$aStatistics . 615 0$aMathematical statistics. 615 0$aProbabilities. 615 0$aR (Computer program language). 615 14$aStatistics and Computing/Statistics Programs. 615 24$aProbability and Statistics in Computer Science. 615 24$aProbability Theory and Stochastic Processes. 676 $a519.2 700 $aIacus$b Stefano M$4aut$4http://id.loc.gov/vocabulary/relators/aut$0725042 702 $aYoshida$b Nakahiro$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910300127403321 996 $aSimulation and Inference for Stochastic Processes with YUIMA$91563764 997 $aUNINA