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A Regression Method Based On Characteristic Functions For Numerical Solutions Of Forward Backward Stochastic Differential Equations
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Book Synopsis A Regression Method Based on Characteristic Functions for Numerical Solutions of Forward-Backward Stochastic Differential Equations by : Deng Ding
Download or read book A Regression Method Based on Characteristic Functions for Numerical Solutions of Forward-Backward Stochastic Differential Equations written by Deng Ding and published by . This book was released on 2014 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we develop a theta-discretization of time integrands for numerical solutions of forward-backward stochastic differential equations, and suggest a new set of basic functions to the Least -squares Monte Carlo simulations. This set of basic functions bases on characteristic functions of transitional densities. Numerical experiments are employed by showing the algorithm available, and a empirical formula is pointed out for more general application.
Book Synopsis A Regression-Based Numerical Method for Forward-Backward Stochastic Differential Equations by : Deng Ding
Download or read book A Regression-Based Numerical Method for Forward-Backward Stochastic Differential Equations written by Deng Ding and published by . This book was released on 2014 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a new class of basic functions based on characteristic functions to approximate two kinds of conditional expectations. we give the proofs and the error analysis of the approximations. In terms of such approximations, we employ a theta-discretization of time integrands for numerical solutions of forward-backward stochastic differential equations, and use Least-squares Monte Carlo simulations based on the basic functions. Numerical experiments are employed to show the algorithm available, and an empirical formula is pointed out for more general application.
Book Synopsis Data Analysis and Related Applications 4 by : Yiannis Dimotikalis
Download or read book Data Analysis and Related Applications 4 written by Yiannis Dimotikalis and published by John Wiley & Sons. This book was released on 2024-10-08 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Stochastic Differential Equations by : Simo Särkkä
Download or read book Applied Stochastic Differential Equations written by Simo Särkkä and published by Cambridge University Press. This book was released on 2019-05-02 with total page 327 pages. 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.
Book Synopsis Backward Stochastic Differential Equations by : N El Karoui
Download or read book Backward Stochastic Differential Equations written by N El Karoui and published by CRC Press. This book was released on 1997-01-17 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the texts of seminars presented during the years 1995 and 1996 at the Université Paris VI and is the first attempt to present a survey on this subject. Starting from the classical conditions for existence and unicity of a solution in the most simple case-which requires more than basic stochartic calculus-several refinements on the hypotheses are introduced to obtain more general results.
Book Synopsis Numerical Methods for Nonlinear Regression by : David Royce Sadler
Download or read book Numerical Methods for Nonlinear Regression written by David Royce Sadler and published by . This book was released on 1975 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Alternative Methods of Regression by : David Birkes
Download or read book Alternative Methods of Regression written by David Birkes and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data sets real. Topics include: multi-response parameter estimation; models defined by systems of differential equations; and improved methods for presenting inferential results of nonlinear analysis. 1988 (0-471-81643-4) 365 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild ".[a] comprehensive and scholarly work.impressively thorough with attention given to every aspect of the modeling process." --Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range of estimation techniques including least squares, quasi-likelihood, and Bayesian methods, and discuss some of the problems associated with estimation. The book presents new and important material relating to the concept of curvature and its growing role in statistical inference. It also covers three useful classes of models --growth, compartmental, and multiphase --and emphasizes the limitations involved in fitting these models. Packed with examples and graphs, it offers statisticians, statistical consultants, and statistically oriented research scientists up-to-date access to their fields. 1989 (0-471-61760-1) 768 pp. Mathematical Programming in Statistics T. S. Arthanari and Yadolah Dodge "The authors have achieved their stated intention.in an outstanding and useful manner for both students and researchers.Contains a superb synthesis of references linked to the special topics and formulations by a succinct set of bibliographical notes.Should be in the hands of all system analysts and computer system architects." --Computing Reviews This unique book brings together most of the available results on applications of mathematical programming in statistics, and also develops the necessary statistical and programming theory and methods. 1981 (0-471-08073-X) 413 pp.
Download or read book Mathematical Reviews written by and published by . This book was released on 2007 with total page 984 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Regression Analysis written by Ashish Sen and published by Springer Science & Business Media. This book was released on 1997-04-01 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interests lie primarily with applications. It is further enhanced through real-life examples drawn from many disciplines, showing the difficulties typically encountered in the practice of regression analysis. Consequently, this book provides a sound foundation in the theory of this important subject.
Book Synopsis Stochastic Approximation and Nonlinear Regression by : Arthur E. Albert
Download or read book Stochastic Approximation and Nonlinear Regression written by Arthur E. Albert and published by MIT Press (MA). This book was released on 2003-02-01 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph addresses the problem of "real-time" curve fitting in the presence of noise, from the computational and statistical viewpoints. It examines the problem of nonlinear regression, where observations are made on a time series whose mean-value function is known except for a vector parameter. In contrast to the traditional formulation, data are imagined to arrive in temporal succession. The estimation is carried out in real time so that, at each instant, the parameter estimate fully reflects all available data.Specifically, the monograph focuses on estimator sequences of the so-called differential correction type. The term "differential correction" refers to the fact that the difference between the components of the updated and previous estimators is proportional to the difference between the current observation and the value that would be predicted by the regression function if the previous estimate were in fact the true value of the unknown vector parameter. The vector of proportionality factors (which is generally time varying and can depend upon previous estimates) is called the "gain" or "smoothing" vector.The main purpose of this research is to relate the large-sample statistical behavior of such estimates (consistency, rate of convergence, large-sample distribution theory, asymptotic efficiency) to the properties of the regression function and the choice of smoothing vectors. Furthermore, consideration is given to the tradeoff that can be effected between computational simplicity and statistical efficiency through the choice of gains.Part I deals with the special cases of an unknown scalar parameter-discussing probability-one and mean-square convergence, rates of mean-square convergence, and asymptotic distribution theory of the estimators for various choices of the smoothing sequence. Part II examines the probability-one and mean-square convergence of the estimators in the vector case for various choices of smoothing vectors. Examples are liberally sprinkled throughout the book. Indeed, the last chapter is devoted entirely to the discussion of examples at varying levels of generality.If one views the stochastic approximation literature as a study in the asymptotic behavior of solutions to a certain class of nonlinear first-order difference equations with stochastic driving terms, then the results of this monograph also serve to extend and complement many of the results in that literature, which accounts for the authors' choice of title.The book is written at the first-year graduate level, although this level of maturity is not required uniformly. Certainly the reader should understand the concept of a limit both in the deterministic and probabilistic senses (i.e., almost sure and quadratic mean convergence). This much will assure a comfortable journey through the first fourth of the book. Chapters 4 and 5 require an acquaintance with a few selected central limit theorems. A familiarity with the standard techniques of large-sample theory will also prove useful but is not essential. Part II, Chapters 6 through 9, is couched in the language of matrix algebra, but none of the "classical" results used are deep. The reader who appreciates the elementary properties of eigenvalues, eigenvectors, and matrix norms will feel at home.MIT Press Research Monograph No. 42
Book Synopsis An Introduction to Neural Network Methods for Differential Equations by : Neha Yadav
Download or read book An Introduction to Neural Network Methods for Differential Equations written by Neha Yadav and published by Springer. This book was released on 2015-02-26 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed interest of the 1980s. A general introduction to neural networks and learning technologies is presented in Section III. This section also includes the description of the multilayer perceptron and its learning methods. In Section IV, the different neural network methods for solving differential equations are introduced, including discussion of the most recent developments in the field. Advanced students and researchers in mathematics, computer science and various disciplines in science and engineering will find this book a valuable reference source.
Book Synopsis Current Index to Statistics, Applications, Methods and Theory by :
Download or read book Current Index to Statistics, Applications, Methods and Theory written by and published by . This book was released on 1995 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.
Book Synopsis Regression Methods by : Rudolf Jakob Freund
Download or read book Regression Methods written by Rudolf Jakob Freund and published by . This book was released on 1979 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Matrices; Linear models: estimation; Linear models: inference; The "Random" error; Too many variables; Models not strictly linear; General linear models; Regression with grouped data: covariance.
Book Synopsis Numerical Algorithms by : Justin Solomon
Download or read book Numerical Algorithms written by Justin Solomon and published by CRC Press. This book was released on 2015-06-24 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig
Book Synopsis A First Course in the Numerical Analysis of Differential Equations by : A. Iserles
Download or read book A First Course in the Numerical Analysis of Differential Equations written by A. Iserles and published by Cambridge University Press. This book was released on 2009 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: lead the reader to a theoretical understanding of the subject without neglecting its practical aspects. The outcome is a textbook that is mathematically honest and rigorous and provides its target audience with a wide range of skills in both ordinary and partial differential equations." --Book Jacket.
Book Synopsis Regression Analysis (Classic Reprint) by : E. J. Williams
Download or read book Regression Analysis (Classic Reprint) written by E. J. Williams and published by Forgotten Books. This book was released on 2018-01-09 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Excerpt from Regression Analysis The subject of this book is one of the branches Of statistics based on the method of least squares and the analysis of variance. With the increase in the scope Of statistical methods in recent years, certain fairly distinct branches have been developed to meet different needs. Thus, on the one hand, the design of experiments is concerned with providing data from which the effects of various factors andthe random errors affecting them can be most accurately and easily determined. On the other hand, regression analysis enables the effects of various factors to be evaluated from the experimental data even when the experiment does not follow a simple pattern, or when the variables affecting the results cannot be controlled in such a manner as to make possible a designed experiment. Thus, although the methods we shall consider can all be formally described in terms of the analysis of variance, it is more profitable to consider separately the problems In which the regression of one variable on others is of interest. Clearly, such a method of analysis can be adopted, whether or not the data to be interpreted come from a designed experiment. Where the experiment is designed to elucidate the effects Of certain factors, the effects of other factors may be considered through a regression analysis, or by means of the technique Of the analysis of co variance, which enables the effects Of uncontrolled variables to be allowed for and the accuracy of the experiment to be consequently improved. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Book Synopsis Topics in Regression Analysis by : Arthur Stanley Goldberger
Download or read book Topics in Regression Analysis written by Arthur Stanley Goldberger and published by . This book was released on 1968 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
