Relative perturbation bounds with applications to empirical covariance operators

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Jirak, Moritz, and Martin Wahl. “Relative Perturbation Bounds With Applications to Empirical Covariance Operators”. Advances in Mathematics, vol. 412, 2023, p. 108808, https://doi.org/10.1016/j.aim.2022.108808.
Jirak, M., & Wahl, M. (2023). Relative perturbation bounds with applications to empirical covariance operators. Advances in Mathematics, 412, 108808. https://doi.org/10.1016/j.aim.2022.108808
Jirak, Moritz, and Martin Wahl. “Relative Perturbation Bounds With Applications to Empirical Covariance Operators”. Advances in Mathematics 412 (2023): 108808. https://doi.org/10.1016/j.aim.2022.108808.
Jirak M, Wahl M. Relative perturbation bounds with applications to empirical covariance operators. Advances in Mathematics. 2023;412:108808.
Refrences
Title Journal Journal Categories Citations Publication Date
Asymptotically efficient estimation of smooth functionals of covariance operators Journal of the European Mathematical Society
  • Science: Mathematics
  • Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods
  • Science: Mathematics
2021
Perturbation bounds for eigenspaces under a relative gap condition

Proceedings of the American Mathematical Society
  • Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods
  • Science: Mathematics
3 2020
Nonasymptotic upper bounds for the reconstruction error of PCA 2020
Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices 2020
The dimension-free structure of nonhomogeneous random matrices Inventiones mathematicae
  • Science: Mathematics
27 2018
Citations
Title Journal Journal Categories Citations Publication Date
A note on the prediction error of principal component regression in high dimensions

Theory of Probability and Mathematical Statistics
  • Science: Mathematics: Probabilities. Mathematical statistics
2023
Citations Analysis
The category Science: Mathematics: Probabilities. Mathematical statistics 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A note on the prediction error of principal component regression in high dimensions and was published in 2023. The most recent citation comes from a 2023 study titled A note on the prediction error of principal component regression in high dimensions. This article reached its peak citation in 2023, with 1 citations. It has been cited in 1 different journals. Among related journals, the Theory of Probability and Mathematical Statistics cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
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