Minimax and adaptive estimation of general linear functionals under sparsity
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| Format: | Preprint |
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2025
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| _version_ | 1866914511892512768 |
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| author | Xie, Jie Huang, Dongming |
| author_facet | Xie, Jie Huang, Dongming |
| contents | We study nonasymptotic minimax estimation of the linear functional $L(θ)=η^\top θ$ for a high-dimensional $s$-sparse mean vector with an arbitrary loading vector $η$. For symmetric noise with exponentially decaying tails, we derive the sharp minimax rate, explicit in $s$, $η$, the tail parameter, and the noise level. The proposed estimator combines plug-in estimation for coordinates with large loadings and thresholding for coordinates with small loadings, and the matching lower bound is obtained via a loading-dependent sparse prior. For unknown sparsity, we construct an $η$-dependent Lepski-type procedure and show that, for a broad verifiable class of loading vectors, its risk matches the oracle rate up to the optimal logarithmic factor. Explicit examples illustrate how heterogeneity in $η$ changes both the minimax and adaptive rates. We also extend the analysis to non-symmetric noise, hypothesis testing, and estimation with unknown noise variance, where we show that asymmetry can increase the minimax rate in certain examples of $η$. Among these results, the two main technical novelties are the following. First, we extend the sharp lower-bound theory beyond the Gaussian setting via a new $χ^2$ bound for generalized Gaussian distributions. Second, for possibly non-symmetric noise, we derive new lower bounds through a worst-case asymmetric construction. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_25595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Minimax and adaptive estimation of general linear functionals under sparsity Xie, Jie Huang, Dongming Statistics Theory We study nonasymptotic minimax estimation of the linear functional $L(θ)=η^\top θ$ for a high-dimensional $s$-sparse mean vector with an arbitrary loading vector $η$. For symmetric noise with exponentially decaying tails, we derive the sharp minimax rate, explicit in $s$, $η$, the tail parameter, and the noise level. The proposed estimator combines plug-in estimation for coordinates with large loadings and thresholding for coordinates with small loadings, and the matching lower bound is obtained via a loading-dependent sparse prior. For unknown sparsity, we construct an $η$-dependent Lepski-type procedure and show that, for a broad verifiable class of loading vectors, its risk matches the oracle rate up to the optimal logarithmic factor. Explicit examples illustrate how heterogeneity in $η$ changes both the minimax and adaptive rates. We also extend the analysis to non-symmetric noise, hypothesis testing, and estimation with unknown noise variance, where we show that asymmetry can increase the minimax rate in certain examples of $η$. Among these results, the two main technical novelties are the following. First, we extend the sharp lower-bound theory beyond the Gaussian setting via a new $χ^2$ bound for generalized Gaussian distributions. Second, for possibly non-symmetric noise, we derive new lower bounds through a worst-case asymmetric construction. |
| title | Minimax and adaptive estimation of general linear functionals under sparsity |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2509.25595 |