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Non-asymptotic rate for high-dimensional covariance estimation with non-independent missing observations

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  • Park, Seongoh
  • Lim, Johan

Abstract

In this paper, we study non-asymptotic convergence rate of the inverse probability weight estimator of covariance matrix when some values of the data are missing completely at random.

Suggested Citation

  • Park, Seongoh & Lim, Johan, 2019. "Non-asymptotic rate for high-dimensional covariance estimation with non-independent missing observations," Statistics & Probability Letters, Elsevier, vol. 153(C), pages 113-123.
    • Handle: RePEc:eee:stapro:v:153:y:2019:i:c:p:113-123
    DOI: 10.1016/j.spl.2019.06.002
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    References listed on IDEAS

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    1. Cai, T. Tony & Zhang, Anru, 2016. "Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 55-74.
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    Cited by:

    1. Ning Zhang & Jin Yang, 2023. "Sparse precision matrix estimation with missing observations," Computational Statistics, Springer, vol. 38(3), pages 1337-1355, September.

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