Analysis of Multivariate and High-Dimensional Data: Series Number 32 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 32) - Hardcover
Synopsis
This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.
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About the Author
Inge Koch is Associate Professor of Statistics at the University of Adelaide, Australia.
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Analysis of Multivariate and High-Dimensional Data
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Cambridge University Press, 2013
ISBN 10: 0521887933
ISBN 13: 9780521887939
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # FM-9780521887939
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Analysis of Multivariate and High-dimensional Data: Theory and Practice
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Hardcover. Condition: Brand New. 504 pages. 10.50x7.50x1.00 inches. In Stock. Seller Inventory # __0521887933
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Analysis of Multivariate and High-Dimensional Data
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Cambridge University Press, 2014
ISBN 10: 0521887933
ISBN 13: 9780521887939
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Seller: moluna, Greven, Germany
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Condition: New. Big data poses challenges that require both classical multivariate methods and modern machine-learning techniques. This coherent treatment integrates theory with data analysis, visualisation and interpretation of the analysis. Problems, data sets and MATL. Seller Inventory # 151161518
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Analysis of Multivariate and High-Dimensional Data
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Cambridge University Press Dez 2013, 2013
ISBN 10: 0521887933
ISBN 13: 9780521887939
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Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Buch. Condition: Neu. Neuware - 'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines. Seller Inventory # 9780521887939