Mathematician uncovers methods to shrink sampling errors in large-dimensional data sets
Shrinkage of the sample eigenvector h along the line connecting h and m(h)1 in Euclidean space (Left) and projected on the unit sphere (right). Credit: Proceedings of the National Academy of Sciences (2023). DOI: 10.1073/pnas.2207046120
A professor in Florida State University's Department of Mathematics has made a breakthrough that will allow scientists across academic disciplines and financial institutions to shrink sampling…