Inference For Diffusion Processes PDF Download
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Author | : Christiane Fuchs |
Publisher | : Springer Science & Business Media |
Total Pages | : 439 |
Release | : 2013-01-18 |
Genre | : Mathematics |
ISBN | : 3642259693 |
Download Inference for Diffusion Processes Book in PDF, ePub and Kindle
Diffusion processes are a promising instrument for realistically modelling the time-continuous evolution of phenomena not only in the natural sciences but also in finance and economics. Their mathematical theory, however, is challenging, and hence diffusion modelling is often carried out incorrectly, and the according statistical inference is considered almost exclusively by theoreticians. This book explains both topics in an illustrative way which also addresses practitioners. It provides a complete overview of the current state of research and presents important, novel insights. The theory is demonstrated using real data applications.
Author | : Yury A. Kutoyants |
Publisher | : Springer Science & Business Media |
Total Pages | : 493 |
Release | : 2013-03-09 |
Genre | : Mathematics |
ISBN | : 144713866X |
Download Statistical Inference for Ergodic Diffusion Processes Book in PDF, ePub and Kindle
The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.
Author | : B.L.S. Prakasa Rao |
Publisher | : Wiley |
Total Pages | : 0 |
Release | : 2010-05-24 |
Genre | : Mathematics |
ISBN | : 9780470711125 |
Download Statistical Inference for Diffusion Type Processes Book in PDF, ePub and Kindle
Decision making in all spheres of activity involves uncertainty. If rational decisions have to be made, they have to be based on the past observations of the phenomenon in question. Data collection, model building and inference from the data collected, validation of the model and refinement of the model are the key steps or building blocks involved in any rational decision making process. Stochastic processes are widely used for model building in the social, physical, engineering, and life sciences as well as in financial economics. Statistical inference for stochastic processes is of great importance from the theoretical as well as from applications point of view in model building. During the past twenty years, there has been a large amount of progress in the study of inferential aspects for continuous as well as discrete time stochastic processes. Diffusion type processes are a large class of continuous time processes which are widely used for stochastic modelling. the book aims to bring together several methods of estimation of parameters involved in such processes when the process is observed continuously over a period of time or when sampled data is available as generally feasible.
Author | : B. L. S. Prakasa Rao |
Publisher | : John Wiley & Sons |
Total Pages | : 213 |
Release | : 2011-07-05 |
Genre | : Mathematics |
ISBN | : 0470975768 |
Download Statistical Inference for Fractional Diffusion Processes Book in PDF, ePub and Kindle
Stochastic processes are widely used for model building in the social, physical, engineering and life sciences as well as in financial economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications point of view. This book deals with Fractional Diffusion Processes and statistical inference for such stochastic processes. The main focus of the book is to consider parametric and nonparametric inference problems for fractional diffusion processes when a complete path of the process over a finite interval is observable. Key features: Introduces self-similar processes, fractional Brownian motion and stochastic integration with respect to fractional Brownian motion. Provides a comprehensive review of statistical inference for processes driven by fractional Brownian motion for modelling long range dependence. Presents a study of parametric and nonparametric inference problems for the fractional diffusion process. Discusses the fractional Brownian sheet and infinite dimensional fractional Brownian motion. Includes recent results and developments in the area of statistical inference of fractional diffusion processes. Researchers and students working on the statistics of fractional diffusion processes and applied mathematicians and statisticians involved in stochastic process modelling will benefit from this book.
Author | : Grigorios A. Pavliotis |
Publisher | : Springer |
Total Pages | : 345 |
Release | : 2014-11-19 |
Genre | : Mathematics |
ISBN | : 1493913239 |
Download Stochastic Processes and Applications Book in PDF, ePub and Kindle
This book presents various results and techniques from the theory of stochastic processes that are useful in the study of stochastic problems in the natural sciences. The main focus is analytical methods, although numerical methods and statistical inference methodologies for studying diffusion processes are also presented. The goal is the development of techniques that are applicable to a wide variety of stochastic models that appear in physics, chemistry and other natural sciences. Applications such as stochastic resonance, Brownian motion in periodic potentials and Brownian motors are studied and the connection between diffusion processes and time-dependent statistical mechanics is elucidated. The book contains a large number of illustrations, examples, and exercises. It will be useful for graduate-level courses on stochastic processes for students in applied mathematics, physics and engineering. Many of the topics covered in this book (reversible diffusions, convergence to equilibrium for diffusion processes, inference methods for stochastic differential equations, derivation of the generalized Langevin equation, exit time problems) cannot be easily found in textbook form and will be useful to both researchers and students interested in the applications of stochastic processes.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 2015 |
Genre | : |
ISBN | : 9783943556438 |
Download Bayesian Inference for Discretely Observed Diffusion Processes Book in PDF, ePub and Kindle
Author | : N. V. Krylov |
Publisher | : Springer Science & Business Media |
Total Pages | : 314 |
Release | : 2008-09-26 |
Genre | : Science |
ISBN | : 3540709142 |
Download Controlled Diffusion Processes Book in PDF, ePub and Kindle
Stochastic control theory is a relatively young branch of mathematics. The beginning of its intensive development falls in the late 1950s and early 1960s. ~urin~ that period an extensive literature appeared on optimal stochastic control using the quadratic performance criterion (see references in Wonham [76]). At the same time, Girsanov [25] and Howard [26] made the first steps in constructing a general theory, based on Bellman's technique of dynamic programming, developed by him somewhat earlier [4]. Two types of engineering problems engendered two different parts of stochastic control theory. Problems of the first type are associated with multistep decision making in discrete time, and are treated in the theory of discrete stochastic dynamic programming. For more on this theory, we note in addition to the work of Howard and Bellman, mentioned above, the books by Derman [8], Mine and Osaki [55], and Dynkin and Yushkevich [12]. Another class of engineering problems which encouraged the development of the theory of stochastic control involves time continuous control of a dynamic system in the presence of random noise. The case where the system is described by a differential equation and the noise is modeled as a time continuous random process is the core of the optimal control theory of diffusion processes. This book deals with this latter theory.
Author | : Simon Lyons |
Publisher | : |
Total Pages | : |
Release | : 2015 |
Genre | : |
ISBN | : |
Download Inference and Parameter Estimation for Diffusion Processes Book in PDF, ePub and Kindle
Author | : Pierre-Yves Deléamont |
Publisher | : |
Total Pages | : 150 |
Release | : 2017 |
Genre | : |
ISBN | : |
Download Contributions to Inference for Diffusion Processes and Robust Statistics Book in PDF, ePub and Kindle
Author | : Stefano M. Iacus |
Publisher | : Springer |
Total Pages | : 268 |
Release | : 2018-06-01 |
Genre | : Computers |
ISBN | : 3319555693 |
Download Simulation and Inference for Stochastic Processes with YUIMA Book in PDF, ePub and Kindle
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.