Statistical Inference For Ergodic Diffusion Processes
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Statistical Inference For Ergodic Diffusion Processes
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Author : Yury A. Kutoyants
language : en
Publisher:
Release Date : 2014-01-15
Statistical Inference For Ergodic Diffusion Processes written by Yury A. Kutoyants and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Statistical Inference For Ergodic Diffusion Processes
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Author : Yu. A. Kutoyants
language : en
Publisher: Springer Science & Business Media
Release Date : 2004
Statistical Inference For Ergodic Diffusion Processes written by Yu. A. Kutoyants 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 2004 with Mathematics categories.
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.
Asymptotic Theory Of Statistical Inference For Time Series
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Author : Masanobu Taniguchi
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Asymptotic Theory Of Statistical Inference For Time Series written by Masanobu Taniguchi 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 2012-12-06 with Mathematics categories.
The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual AR, MA, and ARMA processes. A wide variety of stochastic processes, including non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described. The authors discuss estimation and testing theory and many other relevant statistical methods and techniques.
Statistical Inferences For Stochasic Processes
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Author : Ishwar V. Basawa
language : en
Publisher: Academic Press
Release Date : 1980-01-28
Statistical Inferences For Stochasic Processes written by Ishwar V. Basawa and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980-01-28 with Mathematics categories.
Introductory examples of stochastic models; Special models; General theory; Further approaches.
Statistical Inference For Ergodic Diffusion Processes
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Author : Yury A. Kutoyants
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Statistical Inference For Ergodic Diffusion Processes written by Yury A. Kutoyants 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 2013-03-09 with Mathematics categories.
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.
Statistical Inference For Fractional Diffusion Processes
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Author : B. L. S. Prakasa Rao
language : en
Publisher: John Wiley & Sons
Release Date : 2011-07-05
Statistical Inference For Fractional Diffusion Processes written by B. L. S. Prakasa Rao 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 2011-07-05 with Mathematics categories.
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.
Statistical Inferences For Stochasic Processes
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Author : Ishwar V. Basawa
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Statistical Inferences For Stochasic Processes written by Ishwar V. Basawa and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Mathematics categories.
Stats Inference Stochasic Process
Inference For Diffusion Processes
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Author : Christiane Fuchs
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-01-18
Inference For Diffusion Processes written by Christiane Fuchs 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 2013-01-18 with Mathematics categories.
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.
Statistical Inference In Financial And Insurance Mathematics With R
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Author : Alexandre Brouste
language : en
Publisher: Elsevier
Release Date : 2017-11-22
Statistical Inference In Financial And Insurance Mathematics With R written by Alexandre Brouste and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-22 with Business & Economics categories.
Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables. Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described. In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text. Examines a range of statistical inference methods in the context of finance and insurance applications Presents the LAN (local asymptotic normality) property of likelihoods Combines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematics Provides the proper description of such statistical experiments and invites readers to seek optimal estimators (performed in R) for such statistical experiments
Statistical Inference For Diffusion Type Processes
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Author : B.L.S. Prakasa Rao
language : en
Publisher: Wiley
Release Date : 2010-05-24
Statistical Inference For Diffusion Type Processes written by B.L.S. Prakasa Rao and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-24 with Mathematics categories.
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.