Analysis of Multivariate and High-Dimensional Data

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Cambridge University Press, 02.12.2013
'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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Inhalt

Multidimensional Data
3
Principal Component Analysis
18
3
70
4
116
Problems for Part I
165
Norms Proximities Features and Dualities
175
6
183
Factor Analysis
223
11
349
Kernel and More Independent Component Methods
381
13
421
14
435
22
442
31
450
Problems for Part III
476
Bibliography
483

8
248
Problems for Part II
286
9
295
Independent Component Analysis
305

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Über den Autor (2013)

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

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