Statistics for High-Dimensional Data

Dear Colleagues,

We live in an era where we have the tools, the computing power, and therefore the ability to collect data that are both massive and high-dimensional. In addition, classic statistical methodology has been developed for small datasets where sample size n is much larger than dimension p and therefore many times they are not suitable to address high-dimensional cases where n is approximately equal to p or in cases where n is less than p. In recent years, there has been an explosion of research papers that have tried to address the methodology gap and develop suitable algorithms that address the issue of a low-sample high-dimensional setting.

In this Special Issue, we invite specialists in the area of statistics for high-dimensional data. Their research could be on estimation, hypothesis testing, regression, variable selection, dimension reduction, clustering, classification, or any other topic of modern multivariate statistics that is developed in order to address issues that arise when the data are high-dimensional. Additionally, we are looking for methodological papers, whether they have computational flavor or provide a theoretical discussion of the methodology.

Prof. Dr. Andreas Artemiou
Guest Editor

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• high dimension
• large p small n
• low sample high dimensional setting
• dimension reduction
• variable selection