Principal Components Analysis

Wolfgang Karl Härdle () and Léopold Simar
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Wolfgang Karl Härdle: Humboldt-Universität zu Berlin, Ladislaus von Bortkiewicz Chair of Statistics
Léopold Simar: Université Catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences

Chapter Chapter 11 in Applied Multivariate Statistical Analysis, 2019, pp 299-336 from Springer

Abstract: Abstract Chapter 10 presented the basic geometric tools needed to produce a lower dimensional description of the rows and columns of a multivariate data matrix. Principal components analysis has the same objective with the exception that the rows of the data matrix $${{\mathcal {X}}}$$ will now be considered as observations from a p-variate random variable X. The principle idea of reducing the dimension of X is achieved through linear combinations.

Date: 2019
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DOI: 10.1007/978-3-030-26006-4_11

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