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Shrinkage Estimation of Higher Order Bochner Integrals

arXiv 2207.06357 ↗First posted 13 Jul 2022math.ST stat.ME stat.MLcited by 0

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Abstract (arXiv)

We consider shrinkage estimation of higher order Hilbert space valued Bochner integrals in a non-parametric setting. We propose estimators that shrink the U-statistic estimator of the Bochner integral towards a pre-specified target element in the Hilbert space. Depending on the degeneracy of the kernel of the U-statistic, we construct consistent shrinkage estimators with fast rates of convergence, and develop oracle inequalities comparing the risks of the the U-statistic estimator and its shrinkage version. Surprisingly, we show that the shrinkage estimator designed by assuming complete degeneracy of the kernel of the U-statistic is a consistent estimator even when the kernel is not complete degenerate. This work subsumes and improves upon Krikamol et al., 2016, JMLR and Zhou et al., 2019, JMVA, which only handle mean element and covariance operator estimation in a reproducing kernel Hilbert space. We also specialize our results to normal mean estimation and show that for d≥ 3, the proposed estimator strictly improves upon the sample mean in terms of the mean squared error.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 28 references that are in the map. First posted on arXiv 13 Jul 2022.

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References · 3

  1. 2017On the Optimality of Kernel-Embedding Based Goodness-of-Fit Testscited by 19
  2. 2014Kernel Mean Shrinkage Estimatorscited by 16
  3. 2014Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settingscited by 5

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