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Simulation and Inference for Stochastic Differential Equations

2009-04-27 Computers
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Release : 2009-04-27
Genre : Computers
Kind : eBook
Book Rating : 399/5 ( reviews)

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Book Synopsis Simulation and Inference for Stochastic Differential Equations by : Stefano M. Iacus

Download or read book Simulation and Inference for Stochastic Differential Equations written by Stefano M. Iacus. This book was released on 2009-04-27. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers. While there are several recent texts available that cover stochastic differential equations, the concentration here on inference makes this book stand out. No other direct competitors are known to date. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners and students with minimal mathematical background. What’s more, because of the many R programs, the information here is appropriate for many mathematically well educated practitioners, too.

Applied Stochastic Differential Equations

2019-05-02 Business & Economics
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Release : 2019-05-02
Genre : Business & Economics
Kind : eBook
Book Rating : 085/5 ( reviews)

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Book Synopsis Applied Stochastic Differential Equations by : Simo Särkkä

Download or read book Applied Stochastic Differential Equations written by Simo Särkkä. This book was released on 2019-05-02. Available in PDF, EPUB and Kindle. Book excerpt: With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice.

Simulation and Inference for Stochastic Processes with YUIMA

2018-06-01 Computers
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Release : 2018-06-01
Genre : Computers
Kind : eBook
Book Rating : 693/5 ( reviews)

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Book Synopsis Simulation and Inference for Stochastic Processes with YUIMA by : Stefano M. Iacus

Download or read book Simulation and Inference for Stochastic Processes with YUIMA written by Stefano M. Iacus. This book was released on 2018-06-01. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Modeling with Itô Stochastic Differential Equations

2007-03-08 Mathematics
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Release : 2007-03-08
Genre : Mathematics
Kind : eBook
Book Rating : 531/5 ( reviews)

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Book Synopsis Modeling with Itô Stochastic Differential Equations by : E. Allen

Download or read book Modeling with Itô Stochastic Differential Equations written by E. Allen. This book was released on 2007-03-08. Available in PDF, EPUB and Kindle. Book excerpt: This book explains a procedure for constructing realistic stochastic differential equation models for randomly varying systems in biology, chemistry, physics, engineering, and finance. Introductory chapters present the fundamental concepts of random variables, stochastic processes, stochastic integration, and stochastic differential equations. These concepts are explained in a Hilbert space setting which unifies and simplifies the presentation.

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

2018-12-07 Mathematics
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Release : 2018-12-07
Genre : Mathematics
Kind : eBook
Book Rating : 850/5 ( reviews)

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Book Synopsis Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA by : Elias T. Krainski

Download or read book Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA written by Elias T. Krainski. This book was released on 2018-12-07. Available in PDF, EPUB and Kindle. Book excerpt: Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

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