In this article, we propose a new estimation methodology to deal with PCA for high-dimension, low-sample-size (HDLSS) data. We first show that HDLSS datasets have different geometric representations depending on whether a ρ-mixing-type dependency appears in variables or not. When the...

In the spiked covariance model for High Dimension Low Sample Size (HDLSS) asymptotics where the dimension tends to infinity while the sample size is fixed, a few largest eigenvalues are assumed to grow as the dimension increases. The rate of growth is crucial as the asymptotic behavior of the...