Our paper “Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models” with Iain Johnstone and Jeha Yang (Stanford) and Matthew McKay (HKUST) has been accepted for publication in Statistica Sinica.
This paper has been selected to be part of Sinica’s invited special session at JSM 2020. Sinica hold an invited session at JSM (a huge stats meeting) only once every 2 years. Last time there were only 3 papers in the Sinica special session.
The work characterises the statistics of sample correlation matrices by providing the asymptotic limits and fluctuations (central limit theorems) for the eigenvalues and eigenvectors of high-dimensional correlation matrices. These are fundamental to a myriad of problems based on principal component analysis (PCA), paving the way towards improved PCA in high dimensions, with relevant ramifications into data science and machine learning, particularly in inference problems from large-dimensional data.