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. Dey, R. and Lee, S. (2019) <doi:10.1016/j.jmva.2019.02.007>.

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Package details

AuthorRounak Dey, Seunggeun Lee
MaintainerRounak Dey <deyrnk@umich.edu>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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hdpca documentation built on Jan. 16, 2021, 5:33 p.m.