# Sparse principal component analysis for high-dimensional stationary time series

@inproceedings{Fujimori2021SparsePC, title={Sparse principal component analysis for high-dimensional stationary time series}, author={Kou Fujimori and Yuichi Goto and Yan Liu and Masanobu Taniguchi}, year={2021} }

We consider the sparse principal component analysis for high-dimensional stationary processes. 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…

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