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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.03390 |
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| _version_ | 1866917529394348032 |
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| author | Deitmar, Ben |
| author_facet | Deitmar, Ben |
| contents | A new method of estimating population linear spectral statistics from high-dimensional data is introduced. When the dimension $d$ grows with the sample size $n$ such that $\frac{d}{n} \to c>0$, the proposed method is the first with proven convergence rate of $\mathcal{O}(n^{\varepsilon - 1})$ for any $\varepsilon > 0$ in a general nonparametric setting. For Gaussian data, a CLT for the estimation error with normalization factor $n$ is shown. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimation of Population Linear Spectral Statistics by Marchenko--Pastur Inversion Deitmar, Ben Statistics Theory A new method of estimating population linear spectral statistics from high-dimensional data is introduced. When the dimension $d$ grows with the sample size $n$ such that $\frac{d}{n} \to c>0$, the proposed method is the first with proven convergence rate of $\mathcal{O}(n^{\varepsilon - 1})$ for any $\varepsilon > 0$ in a general nonparametric setting. For Gaussian data, a CLT for the estimation error with normalization factor $n$ is shown. |
| title | Estimation of Population Linear Spectral Statistics by Marchenko--Pastur Inversion |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2504.03390 |