Multivariate Reduced-Rank Regression: Theory, Methods and Applications

Author:   Gregory C. Reinsel ,  Raja P. Velu ,  Kun Chen
Publisher:   Springer-Verlag New York Inc.
Edition:   2nd ed. 2022
Volume:   225
ISBN:  

9781071627914


Pages:   411
Publication Date:   01 December 2022
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Multivariate Reduced-Rank Regression: Theory, Methods and Applications


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Author:   Gregory C. Reinsel ,  Raja P. Velu ,  Kun Chen
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2nd ed. 2022
Volume:   225
Dimensions:   Width: 15.50cm , Height: 2.30cm , Length: 23.50cm
Weight:   0.880kg
ISBN:  

9781071627914


ISBN 10:   1071627910
Pages:   411
Publication Date:   01 December 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Gregory C. Reinsel (now deceased) was Professor of Statistics at the University of Wisconsin, Madison. He was a fellow of the American Statistical Association. He also author of the book Elements of Multivariate Time Series Analysis, Second Edition, and coauthor, with G.E.P. Box and G.M. Jenkins, of the book Time Series Analysis: Forecasting and Control, Third Edition. Greg will remain the first author, in our gratitude. Raja P. Velu taught business analytics and finance at Syracuse University. The first version of the book was mainly based on his thesis written under the supervision of Professor Reinsel and Professor Dean Wichern. He works in the big data models area with interest in high-dimensional time series and forecasting applications. His book, Algorithmic Trading and Quantitative Strategies, co-authored with practitioners from CITI and JP Morgan Chase, is published by Taylor and Francis. He was recently (2021–2022) a visiting researcher at Google working with the Resource Efficiency Data Science team. Kun Chen is an associate professor in the Department of Statistics at the University of Connecticut. He is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. The first version of the book has had profound influence on his research since his PhD study at the University of Iowa under the supervision of Professor Kung-Sik Chan. His related work has resulted in many publications in statistics, machine learning, and scientific journals and the developed methods have been applied to tackle consequential problems in various fields including public health, ecology, and biological sciences.

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