Автор: Gallager Название: Stochastic Processes ISBN: 1107039754 ISBN-13(EAN): 9781107039759 Издательство: Cambridge Academ Рейтинг: Цена: 11246.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This definitive textbook provides a solid introduction to stochastic processes, covering both theory and applications. It is written by one of the world`s leading information theorists, evolving over twenty years of graduate classroom teaching, and is accompanied by over 300 exercises, with online solutions for instructors.
Автор: Doob J.l. Название: Stochastic processes ISBN: 0471523690 ISBN-13(EAN): 9780471523697 Издательство: Wiley Рейтинг: Цена: 19398.00 р. 27712.00-30% Наличие на складе: Есть (1 шт.) Описание: A systematic account of the development of stochastic processes over the last 20 years. A supplement contained within the text includes a treatment of the various aspects of measure theory. There is also a chapter on the specialized problem of prediction theory.
Автор: Hida Takeyuki Название: Stationary Stochastic Processes. (MN-8): ISBN: 0691621411 ISBN-13(EAN): 9780691621418 Издательство: Wiley Рейтинг: Цена: 5069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Encompassing both introductory and more advanced research material, these notes deal with the author`s contributions to stochastic processes and focus on Brownian motion processes and its derivative white noise. Originally published in 1970. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously
Описание: This book provides a self-contained presentation on the structure of a large class of stable processes, known as self-similar mixed moving averages. The first sections in the book review random variables, stochastic processes, and integrals, moving on to rigidity and flows, and finally ending with mixed moving averages and self-similarity.
Описание: This book is an up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. The concentration of the book is on infinite-horizon discrete-time models, and it also discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models.
Автор: Beenstock, Michael, Felsenstein, Daniel Название: The Econometric Analysis of Non-Stationary Spatial Panel Data ISBN: 3030036138 ISBN-13(EAN): 9783030036133 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This monograph deals with spatially dependent non-stationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis.
Автор: Tomasz Rolski Название: Stationary Random Processes Associated with Point Processes ISBN: 0387905758 ISBN-13(EAN): 9780387905754 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 2 deals with discrete time theory. The first one is to let the reader get acquainted with the main lines of the theory needed in continuous time without being bothered by tech- nical details. Chapter 3 deals with continuous time theory. Three applications of the continuous time theory are given in Chapter 4.
Описание: The theory of random functions is a very important and advanced part of modem probability theory, which is very interesting from the mathematical point of view and has many practical applications.
Описание: Correlation Theory of Stationary and Related Random Functions is an elementary introduction to the most important part of the theory dealing only with the first and second moments of these functions.
Описание: Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative assumptions, such as independence or finite memory, clearly fail. However, it has long been considered too general to be able to make statistical inference. One of the reasons for this is that rates of convergence, even of frequencies to the mean, are not available under this assumption alone. Recently, it has been shown that, while some natural and simple problems, such as homogeneity, are indeed provably impossible to solve if one only assumes that the data is stationary (or stationary ergodic), many others can be solved with rather simple and intuitive algorithms. The latter include clustering and change point estimation among others. In this volume I summarize these results. The emphasis is on asymptotic consistency, since this the strongest property one can obtain assuming stationarity alone. While for most of the problem for which a solution is found this solution is algorithmically realizable, the main objective in this area of research, the objective which is only partially attained, is to understand what is possible and what is not possible to do for stationary time series. The considered problems include homogeneity testing (the so-called two sample problem), clustering with respect to distribution, clustering with respect to independence, change point estimation, identity testing, and the general problem of composite hypotheses testing. For the latter problem, a topological criterion for the existence of a consistent test is presented. In addition, a number of open problems is presented.
Описание: of the spectral density I obtained by applying a certain statistical procedure to the observed values of the variables Xl` . , X , usually depends in n a complicated manner on the cyclic frequency). , are approximated by values of a certain sufficiently simple function 1 = 1
Автор: Kenichi Shimizu Название: Bootstrapping Stationary ARMA-GARCH Models ISBN: 3834809926 ISBN-13(EAN): 9783834809926 Издательство: Springer Рейтинг: Цена: 10760.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Im Jahre 1979 hat Bradley Efron mit seiner Arbeit Bootstrap Methods: Another Look at the Jackknife das Tor zu einem in den vergangenen 30 Jahren intensiv bearbeiteten Forschungsgebiet aufgestoen. Die simulationsbasierte Methode des Bootstraps hat sich in den verschiedensten Bereichen als ein auerordentlich - ?zientes Werkzeug zur Approximation der stochastischen Fluktuation eines Sch- zers um die zu schatzende Groe erwiesen. Prazise Kenntnis dieser stochastischen Fluktuation ist zum Beispiel notwendig, um Kon?denzbereiche fur Schatzer an- geben, die die unbekannte interessierende Groe mit einer vorgegebenen Wa- scheinlichkeit von, sagen wir, 95 oder 99% enthalten. In vielen Fallen und bei korrekter Anwendung ist das Bootstrapverfahren dabei der konkurrierenden und auf der Approximation durch eine Normalverteilung basierenden Methode ub- legen. Die Anzahl der Publikationen im Bereich des Bootstraps ist seit 1979 in einem atemberaubenden Tempo angestiegen. Die wesentliche und im Grunde e- fache Idee des Bootstraps ist die Erzeugung vieler (Pseudo-) Datensatze, die von ihrer wesentlichen stochastischen Struktur dem Ausgangsdatensatz moglichst a- lich sind. Die aktuellen Forschungsinteressen im Umfeld des Bootstraps bewegen sich zu einem groen Teil im Bereich der stochastischen Prozesse. Hier stellt sich die zusatzliche Herausforderung, bei der Erzeugung die Abhangigkeitsstruktur der Ausgangsdaten adaquat zu imitieren. Dabei ist eine prazise Analyse der zugrunde liegenden Situation notwendig, um beurteilen zu konnen, welche Abhangigkei- aspekte fur das Verhalten der Schatzer wesentlich sind und welche nicht, um a- reichend komplexe, aber eben auch moglichst einfache Resamplingvorschlage fur die Erzeugung der Bootstrapdaten entwickeln zu konnen.
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