Автор: Durbin, James; Koopman, Siem Jan Название: Time Series Analysis by State Space Methods ISBN: 019964117X ISBN-13(EAN): 9780199641178 Издательство: Oxford Academ Рейтинг: Цена: 18216.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This new edition updates Durbin & Koopman`s important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.
Автор: Hamilton, James Название: Time Series Analysis ISBN: 0691042896 ISBN-13(EAN): 9780691042893 Издательство: Wiley Рейтинг: Цена: 11088.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.
Автор: Casals Jose Manuel Carro Название: State-Space Methods for Time Series Analysis ISBN: 148221959X ISBN-13(EAN): 9781482219593 Издательство: Taylor&Francis Рейтинг: Цена: 15312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values.
Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form.
After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables.
Web Resource The authors' E4 MATLAB(R) toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.
Автор: V?ctor G?mez Название: Multivariate Time Series With Linear State Space Structure ISBN: 331928598X ISBN-13(EAN): 9783319285986 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a comprehensive study of multivariate time serieswith linear state space structure. The strength of the book also lies in the numerous algorithms includedfor state space models that take advantage of the recursive nature of themodels.
Описание: This brief is a clear, concise description of the main techniques of time series analysis —stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.— as they are applied to the influence of wind speed and solar radiation on the production of electrical energy from these renewable sources. The problem of implementing prediction models is addressed by using the embedding-phase-space approach: a powerful technique for the modeling of complex systems. Readers are also guided in applying the main machine learning techniques for classification of the patterns hidden in their time series and so will be able to perform statistical analyses that are not possible by using conventional techniques.The conceptual exposition avoids unnecessary mathematical details and focuses on concrete examples in order to ensure a better understanding of the proposed techniques.Results are well-illustrated by figures and tables.
Автор: Kohei Ohtsu; Hui Peng; Genshiro Kitagawa Название: Time Series Modeling for Analysis and Control ISBN: 4431553029 ISBN-13(EAN): 9784431553021 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Although ships` autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area.
Автор: Commandeur, Jacques J.F.; Koopman, Siem Jan Название: An Introduction to State Space Time Series Analysis ISBN: 0199228876 ISBN-13(EAN): 9780199228874 Издательство: Oxford Academ Рейтинг: Цена: 7681.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text provides an introduction to time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. This is the first in a series of books designed to provide practitioners, researchers, and students with practical introductions to various topics in econometrics.
Автор: Masanao Aoki; Arthur M. Havenner Название: Applications of Computer Aided Time Series Modeling ISBN: 0387947515 ISBN-13(EAN): 9780387947518 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: To single out one of the several important insights in modeling that he shares with the reader, he discusses in Section 2ii the effects of sampling er- rors and model misspecification on successful modeling efforts.
Автор: Barnett William a., Hendry David F., Hylleberg Svend Название: Nonlinear Econometric Modeling in Time Series Analysis ISBN: 0521594243 ISBN-13(EAN): 9780521594240 Издательство: Cambridge Academ Цена: 17266.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.
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