000			03034nam a2200385 i 4500
001			CR9781139025805
003			UkCbUP
005			20240807185053.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110218s2014||||enk o ||1 0|eng|d
020			_a9781139025805 (ebook)
020			_z9780521887939 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA278 _b.K5935 2014
082	0	0	_a519.5/35 _223
100	1		_aKoch, Inge, _d1952- _eauthor.
245	1	0	_aAnalysis of multivariate and high-dimensional data / _cInge Koch, University of Adelaide, Australia.
246	3		_aAnalysis of Multivariate & High-Dimensional Data
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (xxv, 504 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aCambridge series on statistical and probabilistic mathematics ; _v32
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_aMachine 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	_aMultivariate analysis.
650		0	_aBig data.
776	0	8	_iPrint version: _z9780521887939
830		0	_aCambridge series on statistical and probabilistic mathematics ; _v32.
856	4	0	_uhttps://doi.org/10.1017/CBO9781139025805
942			_2ddc _cEB
999			_c8905 _d8905