Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.
The first technique, sometimes called classical, computes eigenvalues and eigenvectors from a covariance matrix derived from the source data. The second PCA technique sidesteps the covariance matrix ...
Froot, K. A. "Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Cross-Sectional Financial Data." Journal of Financial and Quantitative Analysis 24, no.
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