Boundary behavior in High Dimension, Low Sample Size asymptotics of PCA

@article{Jung2012BoundaryBI,
  title={Boundary behavior in High Dimension, Low Sample Size asymptotics of PCA},
  author={Sungkyu Jung and Arusharka Sen and J. S. Marron},
  journal={J. Multivariate Analysis},
  year={2012},
  volume={109},
  pages={190-203}
}
In High Dimension, Low Sample Size (HDLSS) data situations, where the dimension d is much larger than the sample size n, principal component analysis (PCA) plays an important role in statistical analysis. Under which conditions does the sample PCA well reflect the population covariance structure? We answer this question in a relevant asymptotic context where d grows and n is fixed, under a generalized spiked covariance model. Specifically, we assume the largest population eigenvalues to be of… CONTINUE READING
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