3.7 Principal Component Analysis

3.7 Principal Component Analysis With principal component analysis, we transform a random vector Z with correlated components Zi into a random vector D with uncorrelated components Di. This is called an orthogonalization of Z. Principal component analysis can be performed on any random vector Z whose second moments exist, but it is most useful with multicollinear … Continue reading 3.7 Principal Component Analysis

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