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English(EN) Moment-Based Selection of Multiresponse Linear Mixed-Effects Models

新的MOMENT框架增强了多响应线性混合效应模型选择

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的框架MOMENT，用于多响应线性混合效应模型的参数选择和估计。这种分阶段的方法利用二阶交叉矩恒等式来有效地确定随机效应协方差矩阵和固定效应系数。该方法在正半定约束下产生稀疏性，将选择问题转化为可解的凸优化任务。该过程的理论保证包括随机效应和固定效应选择一致性，模拟表明其在处理相关响应方面具有竞争力且有效，如在血液透析数据集上的应用所示。 AI

排序理由 该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv stat.ML 阅读 →

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新的MOMENT框架增强了多响应线性混合效应模型选择

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该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yifan Chen, Yuedong Wang, Guo Yu ·

    多响应线性混合效应模型的基于时刻的选择

    arXiv:2607.01971v1 Announce Type: cross Abstract: We propose MOMENT (\textbf{MO}ment-Based \textbf{M}ixed-\textbf{E}ffects Selectio\textbf{N} and Es\textbf{T}imation), a stage-wise moment-based framework that exploits second-order cross-moment identities to select and estimate th…

  2. arXiv stat.ML TIER_1 English(EN) · Guo Yu ·

    多响应线性混合效应模型的基于时刻的选择

    We propose MOMENT (\textbf{MO}ment-Based \textbf{M}ixed-\textbf{E}ffects Selectio\textbf{N} and Es\textbf{T}imation), a stage-wise moment-based framework that exploits second-order cross-moment identities to select and estimate the random-effects covariance matrix and fixed-effec…

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