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English(EN) Estimation of multiple mean vectors in high dimension

新的统计方法估计高维多均值

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了从独立样本估计多个多维均值的新统计方法。他们的方法利用经验均值的凸组合，其权重由识别低方差邻近均值的检验程序确定，或通过最小化二次风险上的置信上限来确定。理论分析表明，与简单的经验均值相比，这些方法在二次风险方面具有渐近改进，尤其是在数据的有效维度增加时。通过涉及模拟数据和真实数据集的实验，包括多个核均值嵌入的估计，证明了这些技术的有效性。 AI

排序理由 该条目是提交给arXiv的学术论文，详细介绍了统计方法。[lever_c_demoted from research: ic=1 ai=0.4]

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新的统计方法估计高维多均值

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该条目是提交给arXiv的学术论文，详细介绍了统计方法。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Gilles Blanchard (LMO, DATASHAPE), Jean-Baptiste Fermanian (LMO), Hannah Marienwald (TUB) ·

    高维下多个均值向量的估计

    arXiv:2403.15038v3 Announce Type: replace Abstract: We endeavour to estimate numerous multi-dimensional means of various probability distributions on a common space based on independent samples. Our approach involves forming estimators through convex combinations of empirical mea…

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