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 le...

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1. Verfasser: Koch, Inge (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Cambridge Cambridge University Press 2014
Schriftenreihe:Cambridge series on statistical and probabilistic mathematics 32
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Beschreibung
Zusammenfassung:'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
Beschreibung:Title from publisher's bibliographic system (viewed on 05 Oct 2015)
Beschreibung:1 online resource (xxv, 504 pages)
ISBN:9781139025805
DOI:10.1017/CBO9781139025805