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Analysis of multivariate and high-dimensional data /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Analysis of multivariate and high-dimensional data // Inge Koch, University of Adelaide, Australia.
remainder title:	Analysis of Multivariate & High-Dimensional Data
Author:	Koch, Inge,
Description:	1 online resource (xxv, 504 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject:	Multivariate analysis. -
Online resource:	https://doi.org/10.1017/CBO9781139025805
ISBN:	9781139025805 (ebook)

Analysis of multivariate and high-dimensional data /
Koch, Inge,1952-

Analysis of multivariate and high-dimensional data /Analysis of Multivariate & High-Dimensional DataInge Koch, University of Adelaide, Australia. - 1 online resource (xxv, 504 pages) :digital, PDF file(s). - Cambridge series on statistical and probabilistic mathematics ;32. - Cambridge series on statistical and probabilistic mathematics ;32..

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Machine generated contents note: 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.

'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.

ISBN: 9781139025805 (ebook)Subjects--Topical Terms:

577402
Multivariate analysis.

LC Class. No.: QA278 / .K5935 2014

Dewey Class. No.: 519.5/35

Analysis of multivariate and high-dimensional data /
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300 $a 1 online resource (xxv, 504 pages) : $b digital, PDF file(s).
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500 $a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
505 8 $a Machine generated contents note: 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.
520 $a '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.
650 0 $a Multivariate analysis. $3 577402
650 0 $a Big data. $3 981821
776 0 8 $i Print version: $z 9780521887939
830 0 $a Cambridge series on statistical and probabilistic mathematics ; $v 32. $3 1133567
856 4 0 $u https://doi.org/10.1017/CBO9781139025805