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Confidence Sets for Spectral Projectors of Covariance Matrices

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Abstract

A sample X1,...,Xn consisting of independent identically distributed vectors in ℝp with zero mean and a covariance matrix Σ is considered. The recovery of spectral projectors of high-dimensional covariance matrices from a sample of observations is a key problem in statistics arising in numerous applications. In their 2015 work, V. Koltchinskii and K. Lounici obtained nonasymptotic bounds for the Frobenius norm \(\parallel {P_r} - {\hat P_r}{\parallel _2}\) of the distance between sample and true projectors and studied asymptotic behavior for large samples. More specifically, asymptotic confidence sets for the true projector Pr were constructed assuming that the moment characteristics of the observations are known. This paper describes a bootstrap procedure for constructing confidence sets for the spectral projector Pr of the covariance matrix Σ from given data. This approach does not use the asymptotical distribution of \(\parallel {P_r} - {\hat P_r}{\parallel _2}\) and does not require the computation of its moment characteristics. The performance of the bootstrap approximation procedure is analyzed.

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References

  1. J. Tropp, Found. Comput. Math. 12 (4), 389–434 (2012).

    Article  MathSciNet  Google Scholar 

  2. R. Vershynin, “Introduction to the non-asymptotic analysis of random matrices,” in Compressed Sensing: Theory and Applications (Cambridge Univ. Press, Cambridge, 2012), pp. 210–268.

    Chapter  Google Scholar 

  3. R. van Handel, Trans. Am. Math. Soc. 369 (11), 8161–8178 (2017).

    Article  Google Scholar 

  4. V. Koltchinskii and K. Lounici, “Concentration inequalities and moment bounds for sample covariance operators” (2015). ArXiv:1405.2468.

    MATH  Google Scholar 

  5. V. Koltchinskii and K. Lounici, Ann. Stat. 45 (1), 121–157 (2017).

    Article  Google Scholar 

  6. V. Spokoiny, and M. Zhilova, Ann. Stat. 43 (6), 2653–2675 (2015).

    Article  Google Scholar 

  7. V. Chernozhukov, D. Chetverikov, and K. Kato, Ann. Stat. 41 (6), 2786–2819 (2013).

    Article  Google Scholar 

  8. V. Chernozhukov, D. Chetverikov, and K. Kato, Ann. Probab. 45 (4), 2309–2352 (2017).

    Article  MathSciNet  Google Scholar 

  9. A. Naumov, V. Spokoiny, and V. Ulyanov, “Bootstrap confidence sets for spectral projectors of sample covariance” (2017). arXiv:1703.00871.

    Google Scholar 

  10. F. Götze, A. Naumov, V. Spokoiny, and V. Ulyanov, “Large ball probabilities, Gaussian comparison and anti-concentration,” Bernoulli 25 (2019). arXiv:1708.08663v2.

  11. A. Naumov, V. Spokoiny, Yu. Tavyrikov and V. Ulyanov, “Nonasymptotic estimates for the closeness of Gaussian measures on balls”, Dokl. Math. 98 (2018).

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Correspondence to A. A. Naumov.

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Original Russian Text © A.A. Naumov, V.G. Spokoiny, V.V. Ulyanov, 2018, published in Doklady Akademii Nauk, 2018, Vol. 482, No. 6.

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Naumov, A.A., Spokoiny, V.G. & Ulyanov, V.V. Confidence Sets for Spectral Projectors of Covariance Matrices. Dokl. Math. 98, 511–514 (2018). https://doi.org/10.1134/S1064562418060285

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  • DOI: https://doi.org/10.1134/S1064562418060285

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