ESTIMATING SPARSE EIGENSTRUCTURE FOR HIGH DIMENSIONAL DATA – BIASES AND BALMS
IAIN JOHNSTONE – STANFORD UNIVERSITY
When data is high dimensional, widely used multivariate methods such as principal component analysis can behave in unexpected ways. Upward bias in sample eigenvalues and inconsistency of sample eigenvectors are among the new phenomena that appear. In this expository overview talk, I will try to use (amateur!) graphics and heuristic arguments to explain how these phenomena arise, and some of the things that can be done in response.