May 2015
Intermediate to advanced
384 pages
9h 20m
English
book
Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
by Tailen Hsing, Randall Eubank
Content preview from Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
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Chapter 9Principal components analysis
In Section 1.1, we considered a 
-dimensional random vector 
with 
and covariance matrix 
. If
is the eigenvalue–eigenvector decomposition for the covariance matrix, we saw that 
could be decomposed as
for 
zero mean, uncorrelated random variables having 
. The 
provide a new set of variables that are called the principal components of 
. One may, for example, ...
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