Simulation And Inference For Stochastic Differential Equations
DOWNLOAD
FREE 30 Days
Download Simulation And Inference For Stochastic Differential Equations PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Simulation And Inference For Stochastic Differential Equations book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Simulation And Inference For Stochastic Differential Equations
DOWNLOAD
FREE 30 Days
Author : Stefano M. Iacus
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-27
Simulation And Inference For Stochastic Differential Equations written by Stefano M. Iacus and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-27 with Computers categories.
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.
Simulation And Inference For Stochastic Processes With Yuima
DOWNLOAD
FREE 30 Days
Author : Stefano M. Iacus
language : en
Publisher: Springer
Release Date : 2018-06-01
Simulation And Inference For Stochastic Processes With Yuima written by Stefano M. Iacus and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-01 with Computers categories.
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.
An Introduction To The Numerical Simulation Of Stochastic Di Erential Equations
DOWNLOAD
FREE 30 Days
Author : Desmond J. Higham
language : en
Publisher: SIAM
Release Date : 2021-01-28
An Introduction To The Numerical Simulation Of Stochastic Di Erential Equations written by Desmond J. Higham and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-28 with Mathematics categories.
This book provides a lively and accessible introduction to the numerical solution of stochastic differential equations with the aim of making this subject available to the widest possible readership. It presents an outline of the underlying convergence and stability theory while avoiding technical details. Key ideas are illustrated with numerous computational examples and computer code is listed at the end of each chapter. The authors include 150 exercises, with solutions available online, and 40 programming tasks. Although introductory, the book covers a range of modern research topics, including Itô versus Stratonovich calculus, implicit methods, stability theory, nonconvergence on nonlinear problems, multilevel Monte Carlo, approximation of double stochastic integrals, and tau leaping for chemical and biochemical reaction networks. An Introduction to the Numerical Simulation of Stochastic Differential Equations is appropriate for undergraduates and postgraduates in mathematics, engineering, physics, chemistry, finance, and related disciplines, as well as researchers in these areas. The material assumes only a competence in algebra and calculus at the level reached by a typical first-year undergraduate mathematics class, and prerequisites are kept to a minimum. Some familiarity with basic concepts from numerical analysis and probability is also desirable but not necessary.
Statistical Methods For Stochastic Differential Equations
DOWNLOAD
FREE 30 Days
Author : Mathieu Kessler
language : en
Publisher: CRC Press
Release Date : 2012-05-17
Statistical Methods For Stochastic Differential Equations written by Mathieu Kessler and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-17 with Mathematics categories.
The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations. Written to be accessible to both new students and seasoned researchers, each self-contained chapter starts with introductions to the topic at hand and builds gradually towards discussing recent research. The book covers Wiener-driven equations as well as stochastic differential equations with jumps, including continuous-time ARMA processes and COGARCH processes. It presents a spectrum of estimation methods, including nonparametric estimation as well as parametric estimation based on likelihood methods, estimating functions, and simulation techniques. Two chapters are devoted to high-frequency data. Multivariate models are also considered, including partially observed systems, asynchronous sampling, tests for simultaneous jumps, and multiscale diffusions. Statistical Methods for Stochastic Differential Equations is useful to the theoretical statistician and the probabilist who works in or intends to work in the field, as well as to the applied statistician or financial econometrician who needs the methods to analyze biological or financial time series.
Numerical Analysis Of Systems Of Ordinary And Stochastic Differential Equations
DOWNLOAD
FREE 30 Days
Author : Sergej S. Artemiev
language : en
Publisher: VSP
Release Date : 1997
Numerical Analysis Of Systems Of Ordinary And Stochastic Differential Equations written by Sergej S. Artemiev and has been published by VSP this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Mathematics categories.
This book deals with numerical analysis of systems of both ordinary and stochastic differential equations. The first chapter is devoted to numerical solution problems of the Cauchy problem for stiff ordinary differential equation (ODE) systems by Rosenbrock-type methods (RTMs). Here, general solutions of consistency equations are obtained, which lead to the construction of RTMs from the first to the fourth order. The second chapter deals with statistical simulation problems of the solution of the Cauchy problem for stochastic differential equation (SDE) systems. The mean-square convergence theorem is considered, as well as Taylor expansions of numerical solutions. Also included are applications of numerical methods of SDE solutions to partial differential equations and to analysis and synthesis problems of automated control of stochastic systems.
Inference On The Hurst Parameter And The Variance Of Diffusions Driven By Fractional Brownian Motion
DOWNLOAD
FREE 30 Days
Author : Corinne Berzin
language : en
Publisher: Springer
Release Date : 2014-10-15
Inference On The Hurst Parameter And The Variance Of Diffusions Driven By Fractional Brownian Motion written by Corinne Berzin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-15 with Mathematics categories.
This book is devoted to a number of stochastic models that display scale invariance. It primarily focuses on three issues: probabilistic properties, statistical estimation and simulation of the processes considered. It will be of interest to probability specialists, who will find here an uncomplicated presentation of statistics tools and to those statisticians who wants to tackle the most recent theories in probability in order to develop Central Limit Theorems in this context; both groups will also benefit from the section on simulation. Algorithms are described in great detail, with a focus on procedures that is not usually found in mathematical treatises. The models studied are fractional Brownian motions and processes that derive from them through stochastic differential equations. Concerning the proofs of the limit theorems, the “Fourth Moment Theorem” is systematically used, as it produces rapid and helpful proofs that can serve as models for the future. Readers will also find elegant and new proofs for almost sure convergence. The use of diffusion models driven by fractional noise has been popular for more than two decades now. This popularity is due both to the mathematics itself and to its fields of application. With regard to the latter, fractional models are useful for modeling real-life events such as value assets in financial markets, chaos in quantum physics, river flows through time, irregular images, weather events and contaminant diffusio n problems.
Applied Stochastic Differential Equations
DOWNLOAD
FREE 30 Days
Author : Simo Särkkä
language : en
Publisher: Cambridge University Press
Release Date : 2019-05-02
Applied Stochastic Differential Equations written by Simo Särkkä and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-02 with Business & Economics categories.
With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice.
Proceedings Of The Conference On Stochastic Differential Equations And Applications
DOWNLOAD
FREE 30 Days
Author : Jesse David Mason
language : en
Publisher:
Release Date : 1977
Proceedings Of The Conference On Stochastic Differential Equations And Applications written by Jesse David Mason and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1977 with Mathematics categories.
Stochastic Differential Equations With Markovian Switching
DOWNLOAD
FREE 30 Days
Author : Xuerong Mao
language : en
Publisher: Imperial College Press
Release Date : 2006
Stochastic Differential Equations With Markovian Switching written by Xuerong Mao and has been published by Imperial College Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Mathematics categories.
This textbook provides the first systematic presentation of the theory of stochastic differential equations with Markovian switching. It presents the basic principles at an introductory level but emphasizes current advanced level research trends. The material takes into account all the features of Ito equations, Markovian switching, interval systems and time-lag. The theory developed is applicable in different and complicated situations in many branches of science and industry.
Introduction To Stochastic Differential Equations With Applications To Modelling In Biology And Finance
DOWNLOAD
FREE 30 Days
Author : Carlos A. Braumann
language : en
Publisher: John Wiley & Sons
Release Date : 2019-02-25
Introduction To Stochastic Differential Equations With Applications To Modelling In Biology And Finance written by Carlos A. Braumann and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-25 with Mathematics categories.
A comprehensive introduction to the core issues of stochastic differential equations and their effective application Introduction to Stochastic Differential Equations with Applications to Modelling in Biology and Finance offers a comprehensive examination to the most important issues of stochastic differential equations and their applications. The author — a noted expert in the field — includes myriad illustrative examples in modelling dynamical phenomena subject to randomness, mainly in biology, bioeconomics and finance, that clearly demonstrate the usefulness of stochastic differential equations in these and many other areas of science and technology. The text also features real-life situations with experimental data, thus covering topics such as Monte Carlo simulation and statistical issues of estimation, model choice and prediction. The book includes the basic theory of option pricing and its effective application using real-life. The important issue of which stochastic calculus, Itô or Stratonovich, should be used in applications is dealt with and the associated controversy resolved. Written to be accessible for both mathematically advanced readers and those with a basic understanding, the text offers a wealth of exercises and examples of application. This important volume: Contains a complete introduction to the basic issues of stochastic differential equations and their effective application Includes many examples in modelling, mainly from the biology and finance fields Shows how to: Translate the physical dynamical phenomenon to mathematical models and back, apply with real data, use the models to study different scenarios and understand the effect of human interventions Conveys the intuition behind the theoretical concepts Presents exercises that are designed to enhance understanding Offers a supporting website that features solutions to exercises and R code for algorithm implementation Written for use by graduate students, from the areas of application or from mathematics and statistics, as well as academics and professionals wishing to study or to apply these models, Introduction to Stochastic Differential Equations with Applications to Modelling in Biology and Finance is the authoritative guide to understanding the issues of stochastic differential equations and their application.