rounakdey / hdpca

Principal Component Analysis in High-Dimensional Data

hdpca - Principal Component Analysis in High-Dimensional Data

In high-dimensional settings:

• Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model.

• Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors.

• Adjust the shrinkage bias in the predicted PC scores.