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English(EN) Targeted Highly Adaptive Lasso Minimum Loss Estimation of Target Functions

新的目标化高度自适应套索方法改进了统计估计

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员引入了一种名为目标化高度自适应套索（Targeted HAL）的新统计方法，用于估计非路径可微函数参数，例如剂量-反应曲线。该方法利用样条基函数和LASSO步骤来逼近目标函数，旨在提高准确性和数据自适应推断。模拟表明，Targeted HAL在偏差和均方误差方面优于现有的HAL插件估计器，提供了一种灵活的方法，而无需参数假设。 AI

影响 引入了一种新颖的统计技术，可以提高机器学习模型的准确性和数据驱动的推断。

排序理由 该集群包含两篇相同的arXiv预印本，详细介绍了一种新的统计方法。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的目标化高度自适应套索方法改进了统计估计

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该集群包含两篇相同的arXiv预印本，详细介绍了一种新的统计方法。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Vanessa Rodriguez, Karla Diaz-Ordaz, Brieuc Lehmann, Mark J. van der Laan ·

    目标函数的目标高度自适应套索最小损失估计

    arXiv:2607.03824v1 Announce Type: cross Abstract: We propose a Targeted Highly Adaptive Lasso for estimation of non-pathwise differentiable functional parameters such as the dose-response curve (DRC) for continuous exposure. We assume the target function lies in the $k$-th order …

  2. arXiv stat.ML TIER_1 English(EN) · Mark J. van der Laan ·

    目标函数的目标高度自适应套索最小损失估计

    We propose a Targeted Highly Adaptive Lasso for estimation of non-pathwise differentiable functional parameters such as the dose-response curve (DRC) for continuous exposure. We assume the target function lies in the $k$-th order smoothness class used to define the $k$-th order H…

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