Lower bounds for invariant statistical models with applications to principal component analysis

Martin Wahl

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arXiv · 2005.06869

Lower bounds for invariant statistical models with applications to principal component analysis

Martin Wahl

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Abstract

This paper develops nonasymptotic information inequalities for the estimation of the eigenspaces of a covariance operator. These results generalize previous lower bounds for the spiked covariance model, and they show that recent upper bounds for models with decaying eigenvalues are sharp. The proof relies on lower bound techniques based on group invariance arguments which can also deal with a variety of other statistical models.

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BibTeX RIS

Martin Wahl. 2020-05-14. Lower bounds for invariant statistical models with applications to principal component analysis. https://arxiv.org/abs/2005.06869

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