Download or read book Multivariate analysis of long memory series in the frequency domain written by Ignacio Norberto Lobato Garcia and published by . This book was released on 1995 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Long Memory Processes written by Jan Beran and published by Springer Science & Business Media. This book was released on 2013-05-14 with total page 892 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Time Series Analysis with Long Memory in View written by Uwe Hassler and published by John Wiley & Sons. This book was released on 2018-09-07 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a simple exposition of the basic time series material, and insights into underlying technical aspects and methods of proof Long memory time series are characterized by a strong dependence between distant events. This book introduces readers to the theory and foundations of univariate time series analysis with a focus on long memory and fractional integration, which are embedded into the general framework. It presents the general theory of time series, including some issues that are not treated in other books on time series, such as ergodicity, persistence versus memory, asymptotic properties of the periodogram, and Whittle estimation. Further chapters address the general functional central limit theory, parametric and semiparametric estimation of the long memory parameter, and locally optimal tests. Intuitive and easy to read, Time Series Analysis with Long Memory in View offers chapters that cover: Stationary Processes; Moving Averages and Linear Processes; Frequency Domain Analysis; Differencing and Integration; Fractionally Integrated Processes; Sample Means; Parametric Estimators; Semiparametric Estimators; and Testing. It also discusses further topics. This book: Offers beginning-of-chapter examples as well as end-of-chapter technical arguments and proofs Contains many new results on long memory processes which have not appeared in previous and existing textbooks Takes a basic mathematics (Calculus) approach to the topic of time series analysis with long memory Contains 25 illustrative figures as well as lists of notations and acronyms Time Series Analysis with Long Memory in View is an ideal text for first year PhD students, researchers, and practitioners in statistics, econometrics, and any application area that uses time series over a long period. It would also benefit researchers, undergraduates, and practitioners in those areas who require a rigorous introduction to time series analysis.
Download or read book Asymptotics Nonparametrics and Time Series written by Subir Ghosh and published by CRC Press. This book was released on 1999-02-18 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian, bootstrap, adaptive, univariate, and multivariate statistical methods, as well as practical uses of Markov chain models."
Download or read book Time Series with Long Memory written by Peter M. Robinson and published by Advanced Texts in Econometrics. This book was released on 2003 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Long memory time series are characterized by a strong dependence between distant events.
Download or read book Multivariate Time Series Analysis and Applications written by William W. S. Wei and published by John Wiley & Sons. This book was released on 2019-03-18 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.
Download or read book Time Series Analysis and Its Applications written by Robert H. Shumway and published by Springer. This book was released on 2017-04-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.
Download or read book Unit Roots and Structural Breaks written by Pierre Perron and published by MDPI. This book was released on 2018-04-13 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Unit Roots and Structural Breaks" that was published in Econometrics
Download or read book Statistical Modeling of Long Memory and Uncontrolled Effects in Neural Recordings written by Alexander Greaves-Tunnell and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific analyses of time series data are often formalized as statistical investigations targeting one or more aspects of a complex underlying dependence structure. In the multivariate time series setting, there are three main aspects of interest: dependence over time, between components of the multivariate observations, and across repeated trials of the experimental protocol. Classical methods for these data may not be equipped to account for issues such as long-range dependence or unexpected variation across experimental settings. On the other hand, it can be difficult to evaluate whether more recent methods, such as those that make use of deep neural networks, have made quantitatively verifiable progress towards alleviating these issues. There is thus an opportunity to develop tools that extend principled statistical perspectives to meet the demand of current practices and problems in applied time series analysis. Motivated by these considerations, this dissertation develops methodology for the identification, estimation, and prediction of scientifically relevant features in the dependence structure of multivariate time series data. While these contributions apply to a wide range of data-analytical settings, corresponding to the broad prevalence of time series data across the sciences, they are motivated in particular by the challenges raised in the analysis of brain activity data. Neuroscientists measure the locally aggregated activity of cortical neurons as electromagnetic waveforms, recording from multiple locations on the brain surface and across repeated recording trials for various subjects or conditions. The resulting data raise challenging, scientifically important questions that can be phrased in terms of the three aspects of time series dependence enumerated above. We first address the topic of dependence over time through the lens of long-range dependent multivariate time series. We develop a statistical criterion for long memory in deep recurrent neural networks, thus offering a principled alternative to the heuristic evaluations based on synthetic data. We show negative results that suggest even deep recurrent neural network models explicitly designed to capture long-range dependencies fail to do so in a standard language modeling setting. This motivates the development of a model for multivariate long memory time series data, which we define in the frequency domain and apply to the analysis of brain activity in different states of consciousness. Second, we develop a framework to model changes in the dependence structure, or functional connectivity, in recordings obtained from a repeated-stimulation experiment in the rhesus macaque cortex. The analysis flexibly captures nonlinear effects and incorporates information about the connectivity between brain regions prior to stimulation. It is further equipped to address questions of stability, informativeness, and similarity among the learned features. The method improves the prediction accuracy of stimulation-induced connectivity change and provides new insights on the factors mediating this response. Finally, we continue our analysis of the brain-stimulation data to reveal previously unreported variation both between subjects and across experimental trials. We show that extensions to a simple regression model, either accounting for a more complex variance structure or estimating unmodeled confounders, can successfully mitigate the dramatic loss in predictive accuracy that results from applying the model to predict the results of previously unobserved experimental trials. Together, the contributions of this dissertation develop the capacity to detect, estimate, and predict scientifically meaningful aspects of the dependence structure in multivariate time series data.
Download or read book Time Series Analysis Univariate and Multivariate Methods written by William W. S. Wei and published by Pearson. This book was released on 2018-03-14 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses of univariate and multivariate time series methods, with coverage of the most recently developed techniques in the field.
Download or read book Statistics for Long Memory Processes written by Jan Beran and published by Routledge. This book was released on 2017-11-22 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, statistical methods, and applications. The material emphasizes basic principles and practical applications and provides an integrated perspective of both theory and practice. This book explores data sets from a wide range of disciplines, such as hydrology, climatology, telecommunications engineering, and high-precision physical measurement. The data sets are conveniently compiled in the index, and this allows readers to view statistical approaches in a practical context. Statistical Methods for Long Term Memory Processes also supplies S-PLUS programs for the major methods discussed. This feature allows the practitioner to apply long memory processes in daily data analysis. For newcomers to the area, the first three chapters provide the basic knowledge necessary for understanding the remainder of the material. To promote selective reading, the author presents the chapters independently. Combining essential methodologies with real-life applications, this outstanding volume is and indispensable reference for statisticians and scientists who analyze data with long-range dependence.
Download or read book Journal of Econometrics written by and published by . This book was released on 2002 with total page 814 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series written by Raquel Prado and published by CRC Press. This book was released on 2021-07-27 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: • Expanded on aspects of core model theory and methodology. • Multiple new examples and exercises. • Detailed development of dynamic factor models. • Updated discussion and connections with recent and current research frontiers.
Download or read book Modelling Non Stationary Economic Time Series written by S. Burke and published by Springer. This book was released on 2005-06-14 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Co-integration, equilibrium and equilibrium correction are key concepts in modern applications of econometrics to real world problems. This book provides direction and guidance to the now vast literature facing students and graduate economists. Econometric theory is linked to practical issues such as how to identify equilibrium relationships, how to deal with structural breaks associated with regime changes and what to do when variables are of different orders of integration.
Download or read book Palgrave Handbook of Econometrics written by Terence C. Mills and published by Springer. This book was released on 2009-06-25 with total page 1406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following theseminal Palgrave Handbook of Econometrics: Volume I , this second volume brings together the finestacademicsworking in econometrics today andexploresapplied econometrics, containing contributions onsubjects includinggrowth/development econometrics and applied econometrics and computing.
Download or read book Research Papers in Statistical Inference for Time Series and Related Models written by Yan Liu and published by Springer Nature. This book was released on 2023-05-31 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.
Download or read book Multivariate Analysis of Hydrologic Processes written by and published by . This book was released on 1986 with total page 1096 pages. Available in PDF, EPUB and Kindle. Book excerpt:
