Yata, Kazuyoshi; Aoshima, Makoto - In: Journal of Multivariate Analysis 105 (2012) 1, pp. 193-215
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...