On the number of principal components in high dimensions Academic Article uri icon

Overview

MeSH Major

  • Light
  • Microscopy
  • Optical Tweezers

abstract

  • © 2018 Biometrika Trust. We consider how many components to retain in principal component analysis when the dimension is much higher than the number of observations. To estimate the number of components, we propose to sequentially test skewness of the squared lengths of residual scores that are obtained by removing leading principal components. The residual lengths are asymptotically left-skewed if all principal components with diverging variances are removed, and right-skewed otherwise. The proposed estimator is shown to be consistent, performs well in high-dimensional simulation studies, and provides reasonable estimates in examples.

publication date

  • June 2018

Research

keywords

  • Academic Article

Identity

Digital Object Identifier (DOI)

  • 10.1093/biomet/asy010

Additional Document Info

start page

  • 389

end page

  • 402

volume

  • 105

number

  • 2