Analysis of multivariate and high-dimensional data / Inge Koch, University of Adelaide, Australia.
By: Koch, Inge.
Series: Cambridge series in statistical and probabilistic mathematics.Description: xxv, 504 pages ; 27 cm.ISBN: 9780521887939 (hardback).Subject(s): Multivariate analysis | Big dataDDC classification: 519.5/35 Online resources: Full-text hereItem type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Book | Skoltech library Shelves | QA278 .K5935 2014 (Browse shelf) | Available | 2000006388 |
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QA276.45.R3 H67 2015 Using R and RStudio for data management, statistical analysis, and graphics / | QA276.8 .S25 1979 Estimation theory with applications to communications and control / | QA278 .C7 2001 Multidimensional scaling / | QA278 .K5935 2014 Analysis of multivariate and high-dimensional data / | QA278.2 .W35 2013 Bayesian and frequentist regression methods / | QA278.5 .J65 2002 Principal component analysis / | QA278.5 .P35 2015 Multiple factor analysis by example using R / |
Includes bibliographical references (pages 483-492) and indexes.
Machine 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.
"'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text integrates the two strands into a coherent treatment, drawing together theory, data, computation and recent research. The theoretical framework includes formal definitions, theorems and proofs, which clearly set out the guaranteed 'safe operating zone' for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: 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. The text is suitable for graduate students in statistics and researchers in data-rich disciplines"--
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