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Use R!: Simulation and Inference for Stochastic Processes with YUIMA - A Comprehensive R Framework for SDEs and Other Stochastic Processes - Iacus, Stefano M. / Yoshida, Nakahiro
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Iacus, Stefano M. / Yoshida, Nakahiro:
Use R!: Simulation and Inference for Stochastic Processes with YUIMA - A Comprehensive R Framework for SDEs and Other Stochastic Processes - Taschenbuch

2018, ISBN: 9783319555676

[ED: Taschenbuch / Paperback], [PU: Springer Springer International Publishing Springer, Berlin], AUSFÜHRLICHERE BESCHREIBUNG: The 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, COGARCH, and Point 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, 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. INHALT: Introduction 1.1 Overview of the project 1.2 Who should read this book? 1.3 Structure of the book 1.4 How to get the R code for this book 1.5 Main contribution to the Yuima package 1.6 Further developments of Yuima Package 1.7 Things to know about R 1.7.1 How to get R 1.7.2 R and S4 objects 1.8 The yuima package 1.8.1 How to obtain the package 1.8.2 The main object and classes 1.8.3 The yuima.model class 1.9 On model specification 1.9.1 Basic model specification 1.9.2 User-specified state and time variables 1.9.3 Specification of parametric models 1.10 Basic facts on simulation 1.10.1 Customization of simulation arguments 1.10.2 Simulation of models with user-specified notation 1.10.3 Simulation of parametric models 1.11 Sampling and simulate 1.11.1 Sampling and subsampling 1.12 How to make data available into a yuima object 1.12.1 Getting data from data providers 1.13 How to extract data from a yuima object 1.14 Time series classes, time data and time stamps 1.14.1 Review of some time series objects in R 1.14.2 How to handle real time stamps 1.14.3 Dates manipulation 1.14.4 Using dates to index time series 1.14.5 Joining two or more time series 1.14.6 Subsetting a time series 1.15 Miscellanea 1.15.1 From Yuima to LATEX 1.15.2 The Yuima GUI Part II Models and Inference 2 Diffusion processes 2.1 One dimensional model specification 2.1.1 Ornstein-Uhlenbeck (OU) 2.1.2 Geometric Brownian motion (gBm) 2.1.3 Vasicek model (VAS) 2.1.4 Constant elasticity of variance (CEV) 2.1.5 Cox-Ingersoll-Ross process (CIR) 2.1.6 Chan-Karolyi-Longstaff-Sanders process (CKLS) 2.1.7 Hyperbolic diffusion processes 2.2 More about simulation 2.3 Space-discretized Euler-Maruyama simulation scheme 2.4 Multidimensional processes 2.4.1 The Heston model 2.5 Parametric inference 2.5.1 Quasi maximum likelihood estimation 2.5.2 Adaptive Bayes estimation 2.6 Example of real data estimation for gBm 2.7 Example of real data estimation for CIR 2.8 Hypotheses testing 2.9 AIC Model Selection 2.9.1 An example of AIC model selection for exchange rates data 2.10 LASSO model selection 2.10.1 An example of Lasso model selection for interest rates data 2.11 Change point estimation 2.11.1 Example of volatility change-point estimation for 2-dimensional SDE's 2.11.2 An example of two stage estimation 2.11.3 Example of volatility change-point estimation in real data 2.12 Asynchronous covariance estimation 2.12.1 Other covariance estimators 2.13 Lead-lag estimation 2.13.1 Application of the lead-lag estimator to real data 2.14 Asymptotic expansion 2.14.1 Asymptotic expansion for general stochastic processes 3 Compound Poisson processes 3.1 Inhomogenous Compound Poisson Process 3.1.1 Linear intensity function 3.1.2 The Weibull model 3.1.3 The exponentially decaying intensity model 3.1.4 Modulated and periodical intensity model 3.1.5 Frequency modulation model 3.2 Multidimensional Compound Poisson Processes 3.2.1 Multivariate Gaussian Jumps 3.2.2 User specified jump distribution 3.3 Estimation 3.3.1 Compound Poisson process with Gaussian jumps 3.3.2 NIG Compound Poisson process 3.3.3 Exponential jump Compound Poisson process 3.3.4 The Weibull Compound Poisson process 4 Stochastic differential equations driven by Lévy processes 4.1 Lévy processes 4.1.1 Infinitely divisible distributions 4.1.2 Infinite divisible distributions, Lévy processes, Lévy-Itô decomposition 4.2 Wiener process 4.3 Compound Poisson process 4.4 Gamma process and its variants 4.4.1 Gamma process 4.4.2 Variance gamma process 4.4.3 Bilateral gamma process 4.4.4 Simulation of gamma processes 4.5 Generalized tempered stable process, tempered a stable process, CGMY process, positive tempered stable process 4.6 Inverse Gaussian process, DE, [SC: 0.00], Neuware, gewerbliches Angebot, H: 235mm, B: 155mm, T: 15mm, 268, [GW: 435g], Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung, Expédition internationale

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Use R!: Simulation and Inference for Stochastic Processes with YUIMA - A Comprehensive R Framework for SDEs and Other Stochastic Processes - Iacus, Stefano M. / Yoshida, Nakahiro
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Iacus, Stefano M. / Yoshida, Nakahiro:
Use R!: Simulation and Inference for Stochastic Processes with YUIMA - A Comprehensive R Framework for SDEs and Other Stochastic Processes - Taschenbuch

2018, ISBN: 9783319555676

[ED: Taschenbuch / Paperback], [PU: Springer Springer International Publishing Springer, Berlin], AUSFÜHRLICHERE BESCHREIBUNG: The 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, COGARCH, and Point 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, 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. INHALT: Introduction 1.1 Overview of the project 1.2 Who should read this book? 1.3 Structure of the book 1.4 How to get the R code for this book 1.5 Main contribution to the Yuima package 1.6 Further developments of Yuima Package 1.7 Things to know about R 1.7.1 How to get R 1.7.2 R and S4 objects 1.8 The yuima package 1.8.1 How to obtain the package 1.8.2 The main object and classes 1.8.3 The yuima.model class 1.9 On model specification 1.9.1 Basic model specification 1.9.2 User-specified state and time variables 1.9.3 Specification of parametric models 1.10 Basic facts on simulation 1.10.1 Customization of simulation arguments 1.10.2 Simulation of models with user-specified notation 1.10.3 Simulation of parametric models 1.11 Sampling and simulate 1.11.1 Sampling and subsampling 1.12 How to make data available into a yuima object 1.12.1 Getting data from data providers 1.13 How to extract data from a yuima object 1.14 Time series classes, time data and time stamps 1.14.1 Review of some time series objects in R 1.14.2 How to handle real time stamps 1.14.3 Dates manipulation 1.14.4 Using dates to index time series 1.14.5 Joining two or more time series 1.14.6 Subsetting a time series 1.15 Miscellanea 1.15.1 From Yuima to LATEX 1.15.2 The Yuima GUI Part II Models and Inference 2 Diffusion processes 2.1 One dimensional model specification 2.1.1 Ornstein-Uhlenbeck (OU) 2.1.2 Geometric Brownian motion (gBm) 2.1.3 Vasicek model (VAS) 2.1.4 Constant elasticity of variance (CEV) 2.1.5 Cox-Ingersoll-Ross process (CIR) 2.1.6 Chan-Karolyi-Longstaff-Sanders process (CKLS) 2.1.7 Hyperbolic diffusion processes 2.2 More about simulation 2.3 Space-discretized Euler-Maruyama simulation scheme 2.4 Multidimensional processes 2.4.1 The Heston model 2.5 Parametric inference 2.5.1 Quasi maximum likelihood estimation 2.5.2 Adaptive Bayes estimation 2.6 Example of real data estimation for gBm 2.7 Example of real data estimation for CIR 2.8 Hypotheses testing 2.9 AIC Model Selection 2.9.1 An example of AIC model selection for exchange rates data 2.10 LASSO model selection 2.10.1 An example of Lasso model selection for interest rates data 2.11 Change point estimation 2.11.1 Example of volatility change-point estimation for 2-dimensional SDE's 2.11.2 An example of two stage estimation 2.11.3 Example of volatility change-point estimation in real data 2.12 Asynchronous covariance estimation 2.12.1 Other covariance estimators 2.13 Lead-lag estimation 2.13.1 Application of the lead-lag estimator to real data 2.14 Asymptotic expansion 2.14.1 Asymptotic expansion for general stochastic processes 3 Compound Poisson processes 3.1 Inhomogenous Compound Poisson Process 3.1.1 Linear intensity function 3.1.2 The Weibull model 3.1.3 The exponentially decaying intensity model 3.1.4 Modulated and periodical intensity model 3.1.5 Frequency modulation model 3.2 Multidimensional Compound Poisson Processes 3.2.1 Multivariate Gaussian Jumps 3.2.2 User specified jump distribution 3.3 Estimation 3.3.1 Compound Poisson process with Gaussian jumps 3.3.2 NIG Compound Poisson process 3.3.3 Exponential jump Compound Poisson process 3.3.4 The Weibull Compound Poisson process 4 Stochastic differential equations driven by Lévy processes 4.1 Lévy processes 4.1.1 Infinitely divisible distributions 4.1.2 Infinite divisible distributions, Lévy processes, Lévy-Itô decomposition 4.2 Wiener process 4.3 Compound Poisson process 4.4 Gamma process and its variants 4.4.1 Gamma process 4.4.2 Variance gamma process 4.4.3 Bilateral gamma process 4.4.4 Simulation of gamma processes 4.5 Generalized tempered stable process, tempered a stable process, CGMY process, positive tempered stable process 4.6 Inverse Gaussian process, DE, [SC: 0.00], Neuware, gewerbliches Angebot, H: 235mm, B: 155mm, T: 15mm, 268, [GW: 435g], Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung, Internationaler Versand

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Simulation and Inference for Stochastic Processes with YUIMA: A Comprehensive R Framework for SDEs and Other Stochastic Processes (Use R!) - Stefano M. Iacus, Nakahiro Yoshida
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Stefano M. Iacus, Nakahiro Yoshida:
Simulation and Inference for Stochastic Processes with YUIMA: A Comprehensive R Framework for SDEs and Other Stochastic Processes (Use R!) - Taschenbuch

2018, ISBN: 9783319555676

[SR: 519820], Paperback, [EAN: 9783319555676], Springer, Springer, Book, [PU: Springer], 2018-06-29, Springer, The 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, COGARCH, and Point 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, 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., 4133, Testing, 4011, Software Design, Testing & Engineering, 3839, Programming, 5, Computers & Technology, 1000, Subjects, 283155, Books, 271582011, Mathematical & Statistical, 4053, Software, 5, Computers & Technology, 1000, Subjects, 283155, Books, 13983, Probability & Statistics, 226699, Applied, 13884, Mathematics, 75, Science & Math, 1000, Subjects, 283155, Books, 468204, Computer Science, 491298, Algorithms, 491300, Artificial Intelligence, 491306, Database Storage & Design, 491308, Graphics & Visualization, 491302, Networking, 491310, Object-Oriented Software Design, 491312, Operating Systems, 491314, Programming Languages, 491316, Software Design & Engineering, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books, 491548, Statistics, 468218, Mathematics, 468216, Science & Mathematics, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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Simulation and Inference for Stochastic Processes with YUIMA : A Comprehensive R Framework for SDEs and Other Stochastic Processes - Nakahiro Yoshida; Stefano M. Iacus
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Simulation and Inference for Stochastic Processes with YUIMA : A Comprehensive R Framework for SDEs and Other Stochastic Processes - gebrauchtes Buch

ISBN: 3319555677

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The 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, Lvy 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. computers,computers and technology,math,mathematics,science and math Mathematics, Springer

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Simulation and Inference for Stochastic Processes with YUIMA - Taschenbuch

2018, ISBN: 9783319555676

ID: 38420162

A Comprehensive R Framework for SDEs and Other Stochastic Processes, 1st ed. 2018, Softcover, Buch, [PU: Springer International Publishing]

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Simulation and Inference for Stochastic Processes with YUIMA

The 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, COGARCH, and Point 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, 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.

Detailangaben zum Buch - Simulation and Inference for Stochastic Processes with YUIMA


EAN (ISBN-13): 9783319555676
ISBN (ISBN-10): 3319555677
Taschenbuch
Erscheinungsjahr: 2017
Herausgeber: Springer International Publishing / Springer-Verlag GmbH

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ISBN/EAN: 9783319555676

ISBN - alternative Schreibweisen:
3-319-55567-7, 978-3-319-55567-6


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