Multivariate Chebyshev Inequality with Estimated Mean and Variance
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Abstract (arXiv)
A variant of the well-known Chebyshev inequality for scalar random variables can be formulated in the case where the mean and variance are estimated from samples. In this paper we present a generalization of this result to multiple dimensions where the only requirement is that the samples are independent and identically distributed. Furthermore, we show that as the number of samples tends to infinity our inequality converges to the theoretical multi-dimensional Chebyshev bound.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.
5 papers.
- 2022Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisonscited by 169
- 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learningcited by 2
- 2023PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantificationcited by 1
- 2021Introducing the structural bases of typicality effects in deep learningcited by 0
- 2021TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networkscited by 0
- 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning18 Jul 2024
- 2023PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification10 Oct 2023
- 2022Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons19 Jan 2022
- 2021TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks16 Oct 2021
- 2021Introducing the structural bases of typicality effects in deep learning7 Jul 2021