Principal Component Analysis for Special Types of Data
In much of statistical inference, it is assumed that a data set consists of n independent observations on one or more random variables, x, and this assumption is often implicit when a PCA is done. Another assumption which also may be made implicitly is that x consists of continuous variables, with perhaps the stronger assumption of multivariate normality if we require to make some formal inference for the PCs.
KeywordsPrincipal Component Analysis Covariance Matrix Singular Value Decomposition Correspondence Analysis Time Series Data
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