Tabular datasets with low-sample-size or many variables are prevalent in biomedicine.
Practitioners in this domain prefer linear or tree-based models over neural networks since …

Single-cell RNA sequencing (scRNA-seq) can determine gene expression in numerous
individual cells simultaneously, promoting progress in the biomedical sciences. However …

In this paper, we consider clustering based on the kernel principal component analysis
(KPCA) for high-dimension, low-sample-size (HDLSS) data. We give theoretical reasons …

In this paper, we study distance covariance, Hilbert–Schmidt covariance (aka Hilbert–
Schmidt independence criterion [In Advances in Neural Information Processing Systems …

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research
through new terminology for discussing the often many possible approaches to the analysis …

Interpoint distance based two sample tests in high dimension Page 1 Bernoulli 27(2), 2021,
1189–1211 https://doi.org/10.3150/20-BEJ1270 Interpoint distance based two sample tests in …

Principal component analysis (PCA) has long been a useful and important tool for
dimension reduction. However, this method must be used with care under certain …

Testing the homogeneity between two samples of functional data is an important task. While
this is feasible for intensely measured functional data, we explain why it is challenging for …

We identify and correct excess dispersion in the leading eigenvector of a sample covariance
matrix when the number of variables vastly exceeds the number of observations. Our …

In this article, we consider clustering based on principal component analysis (PCA) for high‐
dimensional mixture models. We present theoretical reasons why PCA is effective for …