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A Three-Stage PCA Procedure for Sequentially Arriving High-Dimensional Data
We develop a three-stage adaptive procedure for principal component analysis (PCA) when high-dimensional observations are collected sequentially and additional sampling incurs a cost. The procedure balances PCA compression loss against sampling cost while selecting the retained dimension through a p
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