Selected Proceedings of the Symposium on Inference for Stochastic Processes

Selected Proceedings of the Symposium on Inference for Stochastic Processes

Author:

Publisher:

Published: 2008

Total Pages: 355

ISBN-13:

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This e-book is the product of Project Euclid and its mission to advance scholarly communication in the field of theoretical and applied mathematics and statistics. Project Euclid was developed and deployed by the Cornell University Library and is jointly managed by Cornell and the Duke University Press.


Selected Works of C.C. Heyde

Selected Works of C.C. Heyde

Author: Ross Maller

Publisher: Springer Science & Business Media

Published: 2010-09-17

Total Pages: 490

ISBN-13: 1441958231

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In 1945, very early in the history of the development of a rigorous analytical theory of probability, Feller (1945) wrote a paper called “The fundamental limit theorems in probability” in which he set out what he considered to be “the two most important limit theorems in the modern theory of probability: the central limit theorem and the recently discovered ... ‘Kolmogoroff’s cel ebrated law of the iterated logarithm’ ”. A little later in the article he added to these, via a charming description, the “little brother (of the central limit theo rem), the weak law of large numbers”, and also the strong law of large num bers, which he considers as a close relative of the law of the iterated logarithm. Feller might well have added to these also the beautiful and highly applicable results of renewal theory, which at the time he himself together with eminent colleagues were vigorously producing. Feller’s introductory remarks include the visionary: “The history of probability shows that our problems must be treated in their greatest generality: only in this way can we hope to discover the most natural tools and to open channels for new progress. This remark leads naturally to that characteristic of our theory which makes it attractive beyond its importance for various applications: a combination of an amazing generality with algebraic precision.


Statistical Inference for Fractional Diffusion Processes

Statistical Inference for Fractional Diffusion Processes

Author: B. L. S. Prakasa Rao

Publisher: John Wiley & Sons

Published: 2011-07-05

Total Pages: 213

ISBN-13: 0470975768

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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 Inference and Simulation for Spatial Point Processes

Statistical Inference and Simulation for Spatial Point Processes

Author: Jesper Moller

Publisher: CRC Press

Published: 2003-09-25

Total Pages: 320

ISBN-13: 9780203496930

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Spatial point processes play a fundamental role in spatial statistics and today they are an active area of research with many new applications. Although other published works address different aspects of spatial point processes, most of the classical literature deals only with nonparametric methods, and a thorough treatment of the theory and applications of simulation-based inference is difficult to find. Written by researchers at the top of the field, this book collects and unifies recent theoretical advances and examples of applications. The authors examine Markov chain Monte Carlo algorithms and explore one of the most important recent developments in MCMC: perfect simulation procedures.


Long-Range Dependence and Self-Similarity

Long-Range Dependence and Self-Similarity

Author: Vladas Pipiras

Publisher: Cambridge University Press

Published: 2017-04-18

Total Pages: 693

ISBN-13: 1107039460

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A modern and rigorous introduction to long-range dependence and self-similarity, complemented by numerous more specialized up-to-date topics in this research area.


Spatial Statistics and Computational Methods

Spatial Statistics and Computational Methods

Author: Jesper Møller

Publisher: Springer Science & Business Media

Published: 2013-04-17

Total Pages: 217

ISBN-13: 0387218114

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This volume shows how sophisticated spatial statistical and computational methods apply to a range of problems of increasing importance for applications in science and technology. It introduces topics of current interest in spatial and computational statistics, which should be accessible to postgraduate students as well as to experienced statistical researchers.