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Analysis of Multivariate and High-Dimensional Data [Hardcover]

Item : Analysis of Multivariate and High-Dimensional Data [Hardcover]
Price : $85.99 (see below)
Category : Books
Author Author : Koch, Inge
ISBN-10 ISBN-10 : 0521887933
ISBN-13 ISBN-13 : 9780521887939
Publisher Publisher : Cambridge University Press
Pages Pages : 526
Binding Binding : Hardcover
Item ID : 100046696

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 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.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.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. ??Pală{

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