20 citations · 29 across the 9 of their papers we have counts for
4 papers · 1 filter
Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context
Aki Ishii, Kazuyoshi Yata, Makoto Aoshima
A common feature of high-dimensional data is that the data dimension is high, however, the sample size is relatively low. We call such data HDLSS data. In this paper, we study asym…
High-dimensional inference on covariance structures via the extended cross-data-matrix methodology
Kazuyoshi Yata, Makoto Aoshima
In this paper, we consider testing the correlation coefficient matrix between two subsets of high-dimensional variables. We produce a test statistic by using the extended cross-dat…
Principal component analysis based clustering for high-dimension, low-sample-size data
Kazuyoshi Yata, Makoto Aoshima
In this paper, we consider clustering based on principal component analysis (PCA) for high-dimension, low-sample-size (HDLSS) data. We give theoretical reasons why PCA is effective…
High-dimensional quadratic classifiers in non-sparse settings
Makoto Aoshima, Kazuyoshi Yata
We consider high-dimensional quadratic classifiers in non-sparse settings. The target of classification rules is not Bayes error rates in the context. The classifier based on the M…