Time series, Prado, Raquel (university Of California, Santa Cruz, California, Usa) Ferreira, Marco A. R. (virginia Tech, Blacksburg, Usa) West, Mike (duke Universi

Автор: Durbin, James; Koopman, Siem Jan
Название: Time Series Analysis by State Space Methods
ISBN: 019964117X ISBN-13(EAN): 9780199641178
Издательство: Oxford Academ
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Цена: 126720.00 T
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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
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Цена: 73920.00 T
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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: Rakhee Kulshrestha
Название: Mathematical modeling and computation of real-time problems
ISBN: 0367517434 ISBN-13(EAN): 9780367517434
Издательство: Taylor&Francis
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Цена: 163330.00 T
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Описание: This book covers an interdisciplinary approach for understanding mathematical modeling by offering a collection of models, solved problems related to the models the methodologies employed, and the results using projects and case studies with insight into the operation of substantial real-time systems.

Автор: Levendis John D.
Название: Time Series Econometrics: Learning Through Replication
ISBN: 3319982818 ISBN-13(EAN): 9783319982816
Издательство: Springer
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Цена: 111790.00 T
Наличие на складе: Невозможна поставка.
Описание: Finally, students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger. The book contains many worked-out examples, and many data-driven exercises.

Автор: Donald B. Percival, Andrew T. Walden
Название: Spectral Analysis for Univariate Time Series
ISBN: 1107028140 ISBN-13(EAN): 9781107028142
Издательство: Cambridge Academ
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Цена: 97150.00 T
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Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.

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
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Цена: 90810.00 T
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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: Gebhard Kirchg?ssner; J?rgen Wolters; Uwe Hassler
Название: Introduction to Modern Time Series Analysis
ISBN: 3642440290 ISBN-13(EAN): 9783642440298
Издательство: Springer
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Цена: 69830.00 T
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Описание: This book presents modern methods of time series econometrics and their applications to macroeconomics and finance. It includes numerous examples and analyses based on real economic data.

Автор: Manfred Mudelsee
Название: Climate Time Series Analysis
ISBN: 3319044494 ISBN-13(EAN): 9783319044491
Издательство: Springer
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Цена: 158380.00 T
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Описание: Written for climatologists and applied statisticians, this book explains the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. The accuracy of the algorithms is tested by means of Monte Carlo experiments.

Автор: Fan Jianqing, Yao Qiwei
Название: Nonlinear Time Series / Nonparametric and Parametric Methods
ISBN: 0387261427 ISBN-13(EAN): 9780387261423
Издательство: Springer
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Цена: 102480.00 T
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Описание: This book presents the contemporary statistical methods and theory of nonlinear time series analysis. The principal focus is on nonparametric and semiparametric techniques developed in the last decade. It covers the techniques for modelling in state-space, in frequency-domain as well as in time-domain. To reflect the integration of parametric and nonparametric methods in analyzing time series data, the book also presents an up-to-date exposure of some parametric nonlinear models, including ARCH/GARCH models and threshold models. A compact view on linear ARMA models is also provided. Data arising in real applications are used throughout to show how nonparametric approaches may help to reveal local structure in high-dimensional data. Important technical tools are also introduced. The book will be useful for graduate students, application-oriented time series analysts, and new and experienced researchers. It will have the value both within the statistical community and across a broad spectrum of other fields such as econometrics, empirical finance, population biology and ecology. The prerequisites are basic courses in probability and statistics. Jianqing Fan, coauthor of the highly regarded book Local Polynomial Modeling, is Professor of Statistics at the University of North Carolina at Chapel Hill and the Chinese University of Hong Kong. His published work on nonparametric modeling, nonlinear time series, financial econometrics, analysis of longitudinal data, model selection, wavelets and other aspects of methodological and theoretical statistics has been recognized with the Presidents' Award from the Committee of Presidents of Statistical Societies, the Hettleman Prize for Artistic and Scholarly Achievement from the University of North Carolina, and by his election as a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Qiwei Yao is Professor of Statistics at the London School of Economics and Political Science. He is an elected member of the International Statistical Institute, and has served on the editorial boards for the Journal of the Royal Statistical Society (Series B) and the Australian and New Zealand Journal of Statistics.

Автор: Borradaile
Название: Statistics of Earth Science Data
ISBN: 3540436030 ISBN-13(EAN): 9783540436034
Издательство: Springer
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Цена: 95770.00 T
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Описание: This book is intended for both undergraduate and graduate students in all branches of Earth science needing an introduction to any aspect of data treatment in connection with thesis preparation or writing up a project. It will also aid professional earth scientists to make the most of the interpretation of numerical data using spreadsheets and non-specialized commercial software. This is not merely a traditional statistics primer, it covers sampling, time series, orientation data in two and three dimensions and is very well illustrated with meaningful examples.

Автор: Rojas Ignacio, Pomares Hector, Valenzuela Olga
Название: Time Series Analysis and Forecasting: Selected Contributions from Itise 2017
ISBN: 3319969439 ISBN-13(EAN): 9783319969435
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series.The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics.

Автор: Johanna Adison
Название: Time Series: Applications to Finance with R & S-Plus
ISBN: 168117653X ISBN-13(EAN): 9781681176536
Издательство: Gazelle Book Services
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Цена: 217350.00 T
Наличие на складе: Невозможна поставка.
Описание: The study of time series is concerned with time correlation structures. It has diverse applications ranging from oceanography to finance. The celebrated CAPM model and the stochastic volatility model are examples of financial models that contain a time series component. Time series analysis can be useful to see how a given asset, security or economic variable changes over time or how it changes compared to other variables over the same time period. The Financial Time Series applications provide a convenient interface for creating, managing, and manipulating financial time series objects. In the past few years there have been several changes in the financial landscape as well as developments in using time series techniques for financial modelling. The book aims to highlight several of these standard as well as non-standard techniques applied in finance using S-Plus and R as statistical analysis tools. The book covers practical aspects of these models including estimation and testing of the models and shows practical examples. This book is designed to help readers grasp the conceptual underpinnings of time series modelling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds.