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A0250
Title: Sparse principal component analysis for high-dimensional stationary time series Authors:  Kou Fujimori - Shinshu University (Japan) [presenting]
Yuichi Goto - Kyushu University (Japan)
Yan Liu - Waseda University (Japan)
Abstract: The sparse principal component analysis for high-dimensional stationary processes is discussed. The standard principal component analysis performs poorly when the dimension of the process is large. We establish the oracle inequalities for penalized principal component estimators for the processes including heavy-tailed time series. The rate of convergence of the estimators is established. We also elucidate the theoretical rate for choosing the tuning parameter in penalized estimators. The performance of the sparse principal component analysis is demonstrated by numerical simulations.