Gregory C. Reinsel's Books

Multivariate Reduced-Rank Regression

Theory and Applications

By: Raja Velu,Gregory C. Reinsel

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Multivariate Reduced-Rank Regression

Theory and Applications

By: Raja Velu,Gregory C. Reinsel

In the area of multivariate analysis, there are two broad themes that have emerged over time. The analysis typically involves exploring the variations in a set of interrelated variables or investigating the simultaneous relation ships between two or more sets of variables. In either case, the themes involve explicit modeling of the relationships or dimension-reduction of the sets of variables. The multivariate regression methodology and its variants are the preferred tools for the parametric modeling and descriptive tools such as principal components or canonical correlations are the tools used for addressing the dimension-reduction issues. Both act as complementary to each other and data analysts typically want to make use of these tools for a thorough analysis of multivariate data. A technique that combines the two broad themes in a natural fashion is the method of reduced-rank regres sion. This method starts with the classical multivariate regression model framework but recognizes the possibility for the reduction in the number of parameters through a restrietion on the rank of the regression coefficient matrix. This feature is attractive because regression methods, whether they are in the context of a single response variable or in the context of several response variables, are popular statistical tools. The technique of reduced rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.

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Page Count

269

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1475728530

ISBN 13

9781475728538

Time Series Analysis

Forecasting and Control

By: George E. P. Box,Gwilym M. Jenkins,Gregory C. Reinsel,Greta M. Ljung

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Time Series Analysis

Forecasting and Control

By: George E. P. Box,Gwilym M. Jenkins,Gregory C. Reinsel,Greta M. Ljung

Praise for the Fourth Edition "The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control." —Mathematical Reviews Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject. Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include: A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models Numerous examples drawn from finance, economics, engineering, and other related fields The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting Updates to literature references throughout and new end-of-chapter exercises Streamlined chapter introductions and revisions that update and enhance the exposition Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.

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Page Count

712

Publisher

John Wiley & Sons

Published Date

ISBN 10

111867491X

ISBN 13

9781118674918

Elements of Multivariate Time Series Analysis

By: Gregory C. Reinsel

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Elements of Multivariate Time Series Analysis

By: Gregory C. Reinsel

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.

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Page Count

278

Publisher

Springer Science & Business Media

Published Date

ISBN 10

146840198X

ISBN 13

9781468401981

Multivariate Reduced-Rank Regression

Theory, Methods and Applications

By: Gregory C. Reinsel,Raja P. Velu,Kun Chen

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Multivariate Reduced-Rank Regression

Theory, Methods and Applications

By: Gregory C. Reinsel,Raja P. Velu,Kun Chen

This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed. This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance. This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.

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Page Count

420

Publisher

Springer Nature

Published Date

ISBN 10

1071627937

ISBN 13

9781071627938

Time Series Analysis

Forecasting and Control

By: George E. P. Box,Gwilym M. Jenkins,Gregory C. Reinsel,Greta M. Ljung

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Time Series Analysis

Forecasting and Control

By: George E. P. Box,Gwilym M. Jenkins,Gregory C. Reinsel,Greta M. Ljung

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Page Count

709

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118674928

ISBN 13

9781118674925

Matchsticks Contemplating Eternity

Life, Death, and Faith in What Is

By: Gregory W. Brown

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Matchsticks Contemplating Eternity

Life, Death, and Faith in What Is

By: Gregory W. Brown

These essays invite the reader to join the author’s quest for meaning and wonder in the natural world. After the journey out of evangelical fundamentalism recorded in Unified Field Theology, the journey continues. Through years with more darkness than anticipated, the light shines through. And darkness turns out to be the best place for discerning faint and distant lights. The natural forces studied in the search for the unified theory reveal that the creative sustaining power of the universe is real and observable through science. Humility and wonder are found responding to the size of the cosmos. The strangeness of the quanta invites us to mystery and things beyond rational comprehension accompanied by the assurance that our most basic level is as eternal as the universe. Significance appears in caring for each other, a trait which is now found in both the animal and plant world as well. Returning to traditional sources of faith, the Bible remains an amazing library of voices revealing one people’s evolution of understanding of the ultimate. Like practices of other cultures, it invites us to be still and know. Life is indeed good. We are here, together, choosing how we respond to a very real God.

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Page Count

448

Publisher

Wipf and Stock Publishers

Published Date

ISBN 10

166673408X

ISBN 13

9781666734089

Book's by Gregory C. Reinsel

Multivariate Reduced-Rank Regression

Multivariate Reduced-Rank Regression

Time Series Analysis

Time Series Analysis

Elements of Multivariate Time Series Analysis

Elements of Multivariate Time Series Analysis

Multivariate Reduced-Rank Regression

Multivariate Reduced-Rank Regression

Time Series Analysis

Time Series Analysis

Matchsticks Contemplating Eternity

Matchsticks Contemplating Eternity

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