Elements of Multivariate Time Series Analysis

Author: Gregory C. Reinsel

Publisher: Springer Science & Business Media

ISBN: 146840198X

Category: Mathematics

Page: 263

View: 7766

The use of methods of time series analysis in the study of multivariate time series has become of increased interest in recent years. Although the methods are rather well developed and understood for univarjate time series analysis, the situation is not so complete for the multivariate case. This book is designed to introduce the basic concepts and methods that are useful in the analysis and modeling of multivariate time series, with illustrations of these basic ideas. The development includes both traditional topics such as autocovariance and auto correlation matrices of stationary processes, properties of vector ARMA models, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models, and model checking diagnostics for residuals, as well as topics of more recent interest for vector ARMA models such as reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate unit-root models and cointegration structure, and state-space models and Kalman filtering techniques and applications. This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis is not considered here. For that topic, the reader is referred to the excellent books by Jenkins and Watts (1968), Hannan (1970), Priestley (1981), and others.

Zeitreihenanalyse in den Wirtschaftswissenschaften

Author: Klaus Neusser

Publisher: Springer-Verlag

ISBN: 383488653X

Category: Mathematics

Page: 304

View: 7593

Ob Kursentwicklungen von Aktien oder Anleihen, die Entwicklung des Bruttoinlandsproduktes, die Inflationsrate oder die Arbeitslosenquote, die Wirtschaftsseiten der Zeitungen sind voll von Zeitreihen. Wie man solche Zeitreihen analysiert, Muster und Regelmäßigkeiten erkennt und Prognosen für die zukünftige Entwicklung erstellt, zeigt Ihnen dieses Buch. Der Text der 3. Auflage wurde gründlich überarbeitet und ein Kapitel über die Analyse von Zeitreihen im Frequenzbereich hinzugefügt.

Elements of Nonlinear Time Series Analysis and Forecasting

Author: Jan G. De Gooijer

Publisher: Springer

ISBN: 3319432524

Category: Mathematics

Page: 618

View: 4175

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.

Linear Models for Multivariate, Time Series, and Spatial Data

Author: Ronald Christensen

Publisher: Springer Science & Business Media

ISBN: 1475741030

Category: Mathematics

Page: 318

View: 3670

This is a self-contained companion volume to the authors book "Plane Answers to Complex Questions: The Theory of Linear Models". It provides introductions to several topics related to linear model theory: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis (geostatistics). The purpose of this volume is to use the three fundamental ideas of best linear prediction, projections, and Mahalanobis' distance to exploit their properties in examining multivariate, time series and spatial data. Ronald Christensen is Professor of Statistics at the University of New Mexico, and is recognised internationally as an expert in the theory and application of linear models.

Advanced Linear Modeling

Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization

Author: Ronald Christensen

Publisher: Springer Science & Business Media

ISBN: 1475738471

Category: Mathematics

Page: 398

View: 4568

This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis. This second edition adds new material on nonparametric regression, response surface maximization, and longitudinal models. The book provides a unified approach to these disparate subjects and serves as a self-contained companion volume to the author's Plane Answers to Complex Questions: The Theory of Linear Models. Ronald Christensen is Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of linear models having linear structure.

New Introduction to Multiple Time Series Analysis

Author: Helmut Lütkepohl

Publisher: Springer Science & Business Media

ISBN: 9783540262398

Category: Business & Economics

Page: 764

View: 5371

This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.

Time Series Analysis and Its Applications

With R Examples

Author: Robert H. Shumway,David S. Stoffer

Publisher: Springer

ISBN: 144197864X

Category: Mathematics

Page: 596

View: 8442

Time Series Analysis and Its Applications 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 to be useful as a text 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, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. The third edition includes a new section on testing for unit roots and the material on state-space modeling, ARMAX models, and regression with autocorrelated errors have been expanded. Also new to this edition is the enhanced use of the freeware statistical package R. In particular, R code is now included in the text for nearly all of the numerical examples. Data sets and additional R scripts are now provided in one file that may be downloaded via the World Wide Web. This R supplement is a small compressed file that can be loaded easily into R making all the data sets and scripts available to the user with one simple command. The website for the text includes the code used in each example so that the reader may simply copy-and-paste code directly into R. Appendix R, which is new to this edition, provides a reference for the data sets and our R scripts that are used throughout the text. In addition, Appendix R includes a tutorial on basic R commands as well as an R time series tutorial.

Singular Spectrum Analysis for Time Series

Author: Nina Golyandina,Anatoly Zhigljavsky

Publisher: Springer Science & Business Media

ISBN: 3642349137

Category: Mathematics

Page: 120

View: 5452

Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting combining elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA seeks to decompose the original series into a sum of a small number of interpretable components such as trend, oscillatory components and noise. It is based on the singular value decomposition of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity are assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability. The present book is devoted to the methodology of SSA and shows how to use SSA both safely and with maximum effect. Potential readers of the book include: professional statisticians and econometricians, specialists in any discipline in which problems of time series analysis and forecasting occur, specialists in signal processing and those needed to extract signals from noisy data, and students taking courses on applied time series analysis.

Amstat News

Author: N.A

Publisher: N.A


Category: Statistics

Page: N.A

View: 8110

Applied Multivariate Analysis

Author: Neil H. Timm

Publisher: Springer Science & Business Media

ISBN: 0387227717

Category: Mathematics

Page: 695

View: 7200

This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.

Correlated Data Analysis: Modeling, Analytics, and Applications

Author: Peter X. -K. Song

Publisher: Springer Science & Business Media

ISBN: 038771393X

Category: Mathematics

Page: 352

View: 9009

This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas.


Author: Andrew C. Harvey

Publisher: De Gruyter Oldenbourg

ISBN: 9783486230062


Page: 379

View: 7948

Gegenstand des Werkes sind Analyse und Modellierung von Zeitreihen. Es wendet sich an Studierende und Praktiker aller Disziplinen, in denen Zeitreihenbeobachtungen wichtig sind.

Singular Spectrum Analysis

A New Tool in Time Series Analysis

Author: J.B. Elsner,A.A. Tsonis

Publisher: Springer Science & Business Media

ISBN: 1475725140

Category: Business & Economics

Page: 164

View: 4080

The term singular spectrum comes from the spectral (eigenvalue) decomposition of a matrix A into its set (spectrum) of eigenvalues. These eigenvalues, A, are the numbers that make the matrix A -AI singular. The term singular spectrum analysis· is unfortunate since the traditional eigenvalue decomposition involving multivariate data is also an analysis of the singular spectrum. More properly, singular spectrum analysis (SSA) should be called the analysis of time series using the singular spectrum. Spectral decomposition of matrices is fundamental to much the ory of linear algebra and it has many applications to problems in the natural and related sciences. Its widespread use as a tool for time series analysis is fairly recent, however, emerging to a large extent from applications of dynamical systems theory (sometimes called chaos theory). SSA was introduced into chaos theory by Fraedrich (1986) and Broomhead and King (l986a). Prior to this, SSA was used in biological oceanography by Colebrook (1978). In the digi tal signal processing community, the approach is also known as the Karhunen-Loeve (K-L) expansion (Pike et aI., 1984). Like other techniques based on spectral decomposition, SSA is attractive in that it holds a promise for a reduction in the dimen- • Singular spectrum analysis is sometimes called singular systems analysis or singular spectrum approach. vii viii Preface sionality. This reduction in dimensionality is often accompanied by a simpler explanation of the underlying physics.

Time Series Analysis and Forecasting

Selected Contributions from the ITISE Conference

Author: Ignacio Rojas,Héctor Pomares

Publisher: Springer

ISBN: 3319287257

Category: Business & Economics

Page: 384

View: 6344

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide 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 the disciplines of computer science, mathematics, statistics and econometrics.

Statistical Analysis of Financial Data in S-Plus

Author: René Carmona

Publisher: Springer Science & Business Media

ISBN: 0387218246

Category: Business & Economics

Page: 455

View: 6538

This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. Its originality lies in the introduction of tools for the estimation and simulation of heavy tail distributions and copulas, the computation of measures of risk, and the principal component analysis of yield curves. The book is aimed at undergraduate students in financial engineering; master students in finance and MBA's, and to practitioners with financial data analysis concerns.

Medizinische Statistik

Author: Hans J. Trampisch,Jürgen Windeler

Publisher: Springer-Verlag

ISBN: 364256996X

Category: Mathematics

Page: 376

View: 9006

"Statistiken sind merkwürdige Dinge ...", dies wird so mancher Mediziner denken, wenn er sich mit der Biometrie befaßt. Sei es im Rahmen seiner Ausbildung oder im Zuge wissenschaftlicher oder klinischer Studien, Kenntnisse der Statistik und Mathematik sind unentbehrlich für die tägliche Arbeit des Mediziners. Ziel dieses Lehrbuches ist es, den Mediziner systematisch an biometrische Terminologie und Arbeitsmethoden heranzuführen, um ihn schließlich mit den Grundlagen der Wahrscheinlichkeitsrechung vertraut zu machen. Nach der Lektüre dieses Buches hält der Leser ein Werkzeug in den Händen, das ihm bei der Lösung medizinscher Fragestellungen hilft ebenso wie bei der Beschreibung von Ergebnissen wissenschaftlicher Studien und natürlich bei der Doktorarbeit!

Multivariate Statistical Modelling Based on Generalized Linear Models

Author: Ludwig Fahrmeir,Gerhard Tutz

Publisher: Springer Science & Business Media

ISBN: 1489900101

Category: Mathematics

Page: 426

View: 4297

Concerned with the use of generalised linear models for univariate and multivariate regression analysis, this is a detailed introductory survey of the subject, based on the analysis of real data drawn from a variety of subjects such as the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account.

Introductory Time Series with R

Author: Paul S.P. Cowpertwait,Andrew V. Metcalfe

Publisher: Springer Science & Business Media

ISBN: 0387886982

Category: Mathematics

Page: 256

View: 9415

This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.