Analysis of Multivariate and High-Dimensional Data

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9780521887939: Analysis of Multivariate and High-Dimensional Data

'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.

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Descrizione del libro:

'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 Matlab code complete the package. Suitable for master's/graduate students in statistics and working scientists in data-rich disciplines.

L'autore:

Inge Koch is Associate Professor of Statistics at the University of Adelaide, Australia.

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Inge Koch
Editore: CAMBRIDGE UNIVERSITY PRESS, United Kingdom (2014)
ISBN 10: 0521887933 ISBN 13: 9780521887939
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Descrizione libro CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2014. Hardback. Condizione libro: New. 257 x 178 mm. Language: English . Brand New Book. 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. Codice libro della libreria KNV9780521887939

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Descrizione libro CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2014. Hardback. Condizione libro: New. 257 x 178 mm. Language: English . Brand New Book. 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. Codice libro della libreria KNV9780521887939

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Descrizione libro Cambridge University Press 2013-12-02, Cambridge, 2013. hardback. Condizione libro: New. Codice libro della libreria 9780521887939

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Descrizione libro Cambridge University Press, 2013. Condizione libro: New. This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 526 pages, 5 b/w illus. 98 colour illus. 76 tables 138 exercises. BIC Classification: PBT. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 260 x 180 x 29. Weight in Grams: 1282. . 2013. 1st Edition. Hardcover. . . . . . Codice libro della libreria V9780521887939

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Descrizione libro Cambridge University Press. Hardback. Condizione libro: new. BRAND NEW, Analysis of Multivariate and High-Dimensional Data: Theory and Practice, Inge Koch, '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. Codice libro della libreria B9780521887939

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Descrizione libro Cambridge University Press, 2013. HRD. Condizione libro: New. New Book.Shipped from US within 10 to 14 business days. Established seller since 2000. Codice libro della libreria IB-9780521887939

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Descrizione libro Cambridge University Press. Condizione libro: New. This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 526 pages, 5 b/w illus. 98 colour illus. 76 tables 138 exercises. BIC Classification: PBT. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 260 x 180 x 29. Weight in Grams: 1282. . 2013. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. Codice libro della libreria V9780521887939

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Descrizione libro Condizione libro: New. Publisher/Verlag: Cambridge University Press | This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning. | '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. | Part I. Classical Methods: 1. Multidimensional data; 2. Principal component analysis; 3. Canonical correlation analysis; 4. Discriminant analysis; Part II. Factors and Groupings: 5. Norms, proximities, features, and dualities; 6. Cluster analysis; 7. Factor analysis; 8. Multidimensional scaling; Part III. Non-Gaussian Analysis: 9. Towards non-Gaussianity; 10. Independent component analysis; 11. Projection pursuit; 12. Kernel and more independent component methods; 13. Feature selection and principal component analysis revisited; Index. | Format: Hardback | Language/Sprache: english | 1295 gr | 251x167x29 mm | 400 pp. Codice libro della libreria K9780521887939

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