Asymptotic distribution of recursive subspace estimators
Bin Yang, F. Gersemsky · 2002
We derive the asymptotic distribution of recursive subspace estimators. In particular, we study the PAST algorithm for tracking the signal subspace and the Oja (1982) rule for updating the eigenvector corresponding to the largest eigenvalue. Both the decreasing gain and the constant gain case are considered. It turns out that their asymptotic distributions differ from that of the batch eigenvalue decomposition. The asymptotic rate of convergence is also addressed.