Last edited by Mikacage
Wednesday, August 12, 2020 | History

3 edition of Statistical inference for fractional diffusion processes found in the catalog.

Statistical inference for fractional diffusion processes

by B. L. S. Prakasa Rao

  • 385 Want to read
  • 23 Currently reading

Published by Wiley in Chichester, West Sussex .
Written in English

    Subjects:
  • Fractional calculus,
  • Probabilities

  • Edition Notes

    Includes bibliographical references (p. [239]-249) and index.

    StatementB.L.S. Prakasa Rao
    SeriesWiley series in probability and statistics, Wiley series in probability and statistics
    Classifications
    LC ClassificationsQA314 .P73 2010
    The Physical Object
    Paginationxii, 252 p. :
    Number of Pages252
    ID Numbers
    Open LibraryOL24880172M
    ISBN 100470665688
    ISBN 109780470665688
    LC Control Number2010010075
    OCLC/WorldCa659412731

    Purchase Statistical Inference in Financial and Insurance Mathematics with R - 1st Edition. Print Book & E-Book. ISBN ,   A Collection of Fractional Calculus Books (Last updated: 4/8/) Fractional Powers and Fractional Diffusion Processes (Operator Theory: Advances and Applications). Birkhäuser, , ISBN X. Statistical inference for fractional diffusion processes. John Wiley & Sons, , pages, ISBN

    In this paper a stochastic calculus is given for the fractional Brownian motions that have the Hurst parameter in (1/2, 1). A stochastic integral of Itô type is defined for a family of integrands s. B. L. Prakasa Rao, Statistical Inference for Fractional Diffusion Process, Wiley Series in Probability and Statistics, A John Wiley and Sons, View at: MathSciNet; W. Xiao and J. Yu, “Asymptotic theory for estimating drift parameters in the fractional Vasicek models,” Econometric Theory, pp. 1–34,

    tical inference for discretely observed stochastic differential equations (SDEs) where the driving noise has ‘memory’. Classical SDE mod-els for inference assume the driving noise to be Brownian motion, or “white noise”, thus implying a Markov assumption. We focus on the case when the driving noise is a fractional Brownian motion, which is. Let us also mention that statistical inference for SDEs driven by Lévy processes is currently intensively investigated, with financial motivations in mind. The current article is concerned with the estimation problem for equations of the form (1), when the driving process B is a fractional Brownian motion. Let us recall that a fractional.


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Statistical inference for fractional diffusion processes by B. L. S. Prakasa Rao Download PDF EPUB FB2

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 by:   Statistical Inference for Fractional Diffusion Processes (Wiley Series in Probability and Statistics Book ) - Kindle edition by Rao, B.

Prakasa. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Statistical Inference for Fractional Diffusion Processes (Wiley Series in Probability and 1/5(1). 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. "Statistical Inference for Fractional Diffusion Processes looks at statistical inference for stochastic processes modeled by stochastic differential equations driven by fractional Brownian motion.

Other related processes, such as sequential inference, nonparametric and non parametric inference and parametric estimation are also discussed"   Buy Statistical Inference for Fractional Diffusion Processes by Rao, B.

Prakasa online on at best prices. Fast and free shipping free returns cash on delivery available on eligible : B. Prakasa Rao. 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.

Abstract: Let Θ be an open set of ℝ all n ≥ 1, the observation sample X (n) is the function defined by X (n) (x) = x for all x ∈ ∏ i = 1 n observation sample is possibly written as X (n) = (X 1,X n); each coordinate is the identity function on ℝ as well. This book will consider parametric statistical experiments generated by the observation sample X (n) and.

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.

Prakasa Rao BLS () Statistical inference for fractional diffusion processes. Wiley, New York zbMATH Google Scholar Prakasa Rao BLS, Bhat BR (eds) () Stochastic processes and statistical inference.

Statistical Inference for Stochastic Processes is an international journal publishing articles on parametric and nonparametric inference for discrete- and continuous-time stochastic processes, and their applications to biology, chemistry, physics, finance, economics, and other sciences.

Peer review is conducted using Editorial Manager®, supported by a database of. This chapter discusses inference problems for an important class of processes, the class of diffusion processes that serve as probabilistic models of the physical process of diffusion.

An example of a diffusion phenomenon is the motion of small particles suspended in a homogeneous liquid that are under the influence of collisions with the. Books. Among the books he has authored are: Associated Sequences, Demimartingales and Nonparametric Inference, Birkhauser, Springer, Basel (), ix + pp.

Statistical Inference for Fractional Diffusion Processes, John Wiley, Chichester (), xii + pp. A First Course in Probability and Statistics, World Scientific, Singapore,().

Statistical inference for fractional diffusion processes / B.L.S. Prakasa Rao. – (Wiley series in probability and statistics) Summary: ”Statistical Inference for Fractional Diffusion Processes looks at statistical inference for stochastic processes modeled by stochastic differential equations driven by fractional Brownian motion.

Most books about fractional Brownian motion focus on probabilistic properties, says Prakasa Rao (mathematics and statistics, U. of Hyderabad, India), but he looks instead at the statistical inference for stochastic processes, modeled by stochastic differential equations driven by fractional Brownian motion, which he calls fractional diffusion.

Self‐similar processes. Fractional Brownian motion. Stochastic differential equations driven by fBm. Fractional Ornstein–Uhlenbeck‐type process. Mixed fBm. Donsker‐type approximation for fBm with Hurst index H > ½ Simulation of fBm.

Remarks on application of modeling by fBm in mathematical finance. Pathwise integration with respect to fBm. Statistical Inference for Diffusion Type Processes. Oxford Univ. Press. Mathematical Reviews (MathSciNet): MR Zentralblatt MATH: Stochastic Volterra Equation Driven by Wiener Process and Fractional Brownian Motion Wang, Zhi and Yan, Litan, Abstract and.

Other related processes, such as sequential inference, nonparametric and non parametric inference and parametric estimation are also discussed.

The book will deal with Fractional Diffusion Processes (FDP) in relation to statistical influence for stochastic by: Statistical Inference for Ergodic Diffusion Processes encompasses a wealth of results from over ten years of mathematical literature.

It provides a comprehensive overview of existing techniques, and presents - for the first time in book form - many new techniques and approaches. Statistical Inference from Stochastic Processes ( Cornell University) Statistical inference from stochastic processes: proceedings of the AMS-IMS-SIAM joint summer research conference held August, with support from the National Science Foundation and the Army Research Office/N.U.

Prabhu, editor. -(Contemporary mathematics. Brouste A, Kleptsyna M, Popier A: Design for estimation of the drift parameter in fractional diffusion systems.

Stat. Inference Stoch. Process. 15(2)– /s MathSciNet Article Google Scholar. Statistical-Inference-For-Fractional-Diffusion-Va Adobe Acrobat Reader DCDownload Adobe Acrobat Reader DC Ebook PDF:Download free Acrobat Reader DC software the only PDF viewer that lets you read search print and interact with virtually any type of PDF file.().

Statistical Inference for Ergodic Diffusion Processes. Journal of the American Statistical Association: Vol.No.pp. 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.