Articles liés à Analysis of Multivariate and High-Dimensional Data
Synopsis
'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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À propos de l?auteur
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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Analysis of Multivariate and High-Dimensional Data
Edité par
Cambridge University Press, 2013
ISBN 10 : 0521887933
ISBN 13 : 9780521887939
Neuf
Couverture rigide
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur FM-9780521887939
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Analysis of Multivariate and High-dimensional Data: Theory and Practice
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Cambridge Univ Pr, 2014
ISBN 10 : 0521887933
ISBN 13 : 9780521887939
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Analysis of Multivariate and High-Dimensional Data (Hardcover)
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Cambridge University Press, Cambridge, 2013
ISBN 10 : 0521887933
ISBN 13 : 9780521887939
Neuf
Couverture rigide
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. '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. '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 (R) code complete the package. It is suitable for master's/graduate students in statistics and working scientists in data-rich disciplines. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9780521887939
Quantité disponible : 1 disponible(s)
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Analysis of Multivariate and High-Dimensional Data (Hardcover)
Edité par
Cambridge University Press, Cambridge, 2013
ISBN 10 : 0521887933
ISBN 13 : 9780521887939
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. '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. '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 (R) code complete the package. It is suitable for master's/graduate students in statistics and working scientists in data-rich disciplines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9780521887939
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Analysis of Multivariate and High-Dimensional Data (Hardcover)
Edité par
Cambridge University Press, Cambridge, 2013
ISBN 10 : 0521887933
ISBN 13 : 9780521887939
Neuf
Couverture rigide
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. '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. '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 (R) code complete the package. It is suitable for master's/graduate students in statistics and working scientists in data-rich disciplines. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9780521887939
Quantité disponible : 1 disponible(s)