
Summary: Factor analysis, a popular method for interpreting multivariate data, models the covariance among \(p\) variables as being due to a small number (\(k\), \(1 \leq k 1\) and \(\eta\) is rich enough, \(F\) ordered or, at least if \(k = 2\) or 3, unordered, must have a singularity at some data set in \(\mathcal{X}\). The proofs are applications of algebraic topology. Examples are provided.
Numerical smoothing, curve fitting, Fiber spaces and bundles in algebraic topology, Factor analysis and principal components; correspondence analysis
Numerical smoothing, curve fitting, Fiber spaces and bundles in algebraic topology, Factor analysis and principal components; correspondence analysis
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