[HTML][HTML] The statistics and mathematics of high dimension low sample size asymptotics
The aim of this paper is to establish several deep theoretical properties of principal
component analysis for multiple-component spike covariance models. Our new results …
component analysis for multiple-component spike covariance models. Our new results …
A survey of high dimension low sample size asymptotics
Peter Hall's work illuminated many aspects of statistical thought, some of which are very well
known including the bootstrap and smoothing. However, he also explored many other lesser …
known including the bootstrap and smoothing. However, he also explored many other lesser …
When and why are principal component scores a good tool for visualizing high‐dimensional data?
KH Hellton, M Thoresen - Scandinavian Journal of Statistics, 2017 - Wiley Online Library
Principal component analysis is a popular dimension reduction technique often used to
visualize high‐dimensional data structures. In genomics, this can involve millions of …
visualize high‐dimensional data structures. In genomics, this can involve millions of …
Principal component analysis based clustering for high-dimension, low-sample-size data
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 …
dimension, low-sample-size (HDLSS) data. We give theoretical reasons why PCA is …
Integrative clustering of high-dimensional data with joint and individual clusters, with an application to the Metabric study
K Hellton, M Thoresen - arXiv preprint arXiv:1410.8679, 2014 - arxiv.org
When measuring a range of different genomic, epigenomic, transcriptomic and other
variables, an integrative approach to analysis can strengthen inference and give new …
variables, an integrative approach to analysis can strengthen inference and give new …