Titlebook: Applied Multivariate Statistical Analysis; Wolfgang Karl H?rdle,Léopold Simar Textbook 20195th edition Springer Nature Switzerland AG 2019

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Textbook 20195th editionchapters include practical exercises that highlight applications in different multivariate data analysis fields, and all the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis..For this new edition, the book has been updated and extensively rev

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A Short Excursion into Matrix Algebranotations used in this book for vectors and matrices. Eigenvalues and eigenvectors play an important role in multivariate techniques. In Sect.?. and?., we present the spectral decomposition of matrices and consider the maximization (minimization) of quadratic forms given some constraints.

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Multivariate Distributionstion on the relationship between the variables can be made available. Only basic statistical theory was used to derive tests of independence or of linear relationships. In this chapter, we give an introduction to the basic probability tools useful in statistical multivariate analysis.

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Principal Components Analysis. Principal components analysis has the same objective with the exception that the rows of the data matrix . will now be considered as observations from a .-variate random variable .. The principle idea of reducing the dimension of . is achieved through linear combinations.

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