# Sample Efficient Toeplitz Covariance Estimation

@inproceedings{Eldar2020SampleET, title={Sample Efficient Toeplitz Covariance Estimation}, author={Yonina C. Eldar and Jerry Li and Cameron Musco and Christopher Musco}, booktitle={SODA}, year={2020} }

We study the query complexity of estimating the covariance matrix $T$ of a distribution $\mathcal{D}$ over $d$-dimensional vectors, under the assumption that $T$ is Toeplitz. This assumption arises in many signal processing problems, where the covariance between any two measurements only depends on the time or distance between those measurements. We are interested in estimation strategies that may choose to view only a subset of entries in each vector sample $x^{(\ell)} \sim \mathcal{D}$, which… CONTINUE READING

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