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| Author | : Vladas Pipiras |
| Publisher | : Cambridge University Press |
| Total Pages | : 693 |
| Release | : 2017-04-18 |
| Genre | : Business & Economics |
| ISBN | : 1107039460 |
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.
| Author | : Gennady Samorodnitsky |
| Publisher | : Now Publishers Inc |
| Total Pages | : 109 |
| Release | : 2007 |
| Genre | : Mathematics |
| ISBN | : 1601980906 |
Long Range Dependence is a wide ranging survey of the ideas, models and techniques associated with the notion of long memory. It will serve as an invaluable reference source for researchers studying long range dependence, for those building long memory models, and for people who are trying to detect the possible presence of long memory in data.
| Author | : Paul Doukhan |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 744 |
| Release | : 2002-12-13 |
| Genre | : Mathematics |
| ISBN | : 9780817641689 |
The area of data analysis has been greatly affected by our computer age. For example, the issue of collecting and storing huge data sets has become quite simplified and has greatly affected such areas as finance and telecommunications. Even non-specialists try to analyze data sets and ask basic questions about their structure. One such question is whether one observes some type of invariance with respect to scale, a question that is closely related to the existence of long-range dependence in the data. This important topic of long-range dependence is the focus of this unique work, written by a number of specialists on the subject. The topics selected should give a good overview from the probabilistic and statistical perspective. Included will be articles on fractional Brownian motion, models, inequalities and limit theorems, periodic long-range dependence, parametric, semiparametric, and non-parametric estimation, long-memory stochastic volatility models, robust estimation, and prediction for long-range dependence sequences. For those graduate students and researchers who want to use the methodology and need to know the "tricks of the trade," there will be a special section called "Mathematical Techniques." Topics in the first part of the book are covered from probabilistic and statistical perspectives and include fractional Brownian motion, models, inequalities and limit theorems, periodic long-range dependence, parametric, semiparametric, and non-parametric estimation, long-memory stochastic volatility models, robust estimation, prediction for long-range dependence sequences. The reader is referred to more detailed proofs if already found in the literature. The last part of the book is devoted to applications in the areas of simulation, estimation and wavelet techniques, traffic in computer networks, econometry and finance, multifractal models, and hydrology. Diagrams and illustrations enhance the presentation. Each article begins with introductory background material and is accessible to mathematicians, a variety of practitioners, and graduate students. The work serves as a state-of-the art reference or graduate seminar text.
| Author | : Gennady Samorodnitsky |
| Publisher | : Springer |
| Total Pages | : 415 |
| Release | : 2016-11-09 |
| Genre | : Mathematics |
| ISBN | : 3319455753 |
This monograph is a gateway for researchers and graduate students to explore the profound, yet subtle, world of long-range dependence (also known as long memory). The text is organized around the probabilistic properties of stationary processes that are important for determining the presence or absence of long memory. The first few chapters serve as an overview of the general theory of stochastic processes which gives the reader sufficient background, language, and models for the subsequent discussion of long memory. The later chapters devoted to long memory begin with an introduction to the subject along with a brief history of its development, followed by a presentation of what is currently the best known approach, applicable to stationary processes with a finite second moment. The book concludes with a chapter devoted to the author’s own, less standard, point of view of long memory as a phase transition, and even includes some novel results. Most of the material in the book has not previously been published in a single self-contained volume, and can be used for a one- or two-semester graduate topics course. It is complete with helpful exercises and an appendix which describes a number of notions and results belonging to the topics used frequently throughout the book, such as topological groups and an overview of the Karamata theorems on regularly varying functions.
| Author | : Vladas Pipiras |
| Publisher | : Cambridge University Press |
| Total Pages | : 693 |
| Release | : 2017-04-18 |
| Genre | : Mathematics |
| ISBN | : 1108210198 |
This modern and comprehensive guide to long-range dependence and self-similarity starts with rigorous coverage of the basics, then moves on to cover more specialized, up-to-date topics central to current research. These topics concern, but are not limited to, physical models that give rise to long-range dependence and self-similarity; central and non-central limit theorems for long-range dependent series, and the limiting Hermite processes; fractional Brownian motion and its stochastic calculus; several celebrated decompositions of fractional Brownian motion; multidimensional models for long-range dependence and self-similarity; and maximum likelihood estimation methods for long-range dependent time series. Designed for graduate students and researchers, each chapter of the book is supplemented by numerous exercises, some designed to test the reader's understanding, while others invite the reader to consider some of the open research problems in the field today.
| Author | : Jan Beran |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 892 |
| Release | : 2013-05-14 |
| Genre | : Mathematics |
| ISBN | : 3642355129 |
Long-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.
| Author | : Ming Li |
| Publisher | : Frontiers Media SA |
| Total Pages | : 160 |
| Release | : 2022-12-05 |
| Genre | : Science |
| ISBN | : 2832508502 |
| Author | : Eberlein |
| Publisher | : Birkhäuser |
| Total Pages | : 496 |
| Release | : 1986 |
| Genre | : Mathematics |
| ISBN | : |
| Author | : Paul Embrechts |
| Publisher | : Princeton University Press |
| Total Pages | : 128 |
| Release | : 2009-01-10 |
| Genre | : Mathematics |
| ISBN | : 1400825105 |
The modeling of stochastic dependence is fundamental for understanding random systems evolving in time. When measured through linear correlation, many of these systems exhibit a slow correlation decay--a phenomenon often referred to as long-memory or long-range dependence. An example of this is the absolute returns of equity data in finance. Selfsimilar stochastic processes (particularly fractional Brownian motion) have long been postulated as a means to model this behavior, and the concept of selfsimilarity for a stochastic process is now proving to be extraordinarily useful. Selfsimilarity translates into the equality in distribution between the process under a linear time change and the same process properly scaled in space, a simple scaling property that yields a remarkably rich theory with far-flung applications. After a short historical overview, this book describes the current state of knowledge about selfsimilar processes and their applications. Concepts, definitions and basic properties are emphasized, giving the reader a road map of the realm of selfsimilarity that allows for further exploration. Such topics as noncentral limit theory, long-range dependence, and operator selfsimilarity are covered alongside statistical estimation, simulation, sample path properties, and stochastic differential equations driven by selfsimilar processes. Numerous references point the reader to current applications. Though the text uses the mathematical language of the theory of stochastic processes, researchers and end-users from such diverse fields as mathematics, physics, biology, telecommunications, finance, econometrics, and environmental science will find it an ideal entry point for studying the already extensive theory and applications of selfsimilarity.
| Author | : Piotr Gaj |
| Publisher | : Springer |
| Total Pages | : 456 |
| Release | : 2015-05-27 |
| Genre | : Computers |
| ISBN | : 3319194194 |
This book constitutes the thoroughly refereed proceedings of the 22st International Conference on Computer Networks, CN 2015, held in Brunów, Poland, in June 2015. The 42 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers in these proceedings cover the following topics: computer networks, distributed computer systems, communications and teleinformatics.
