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Updated: Mar 18, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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稀疏和不规则的纵向数据的动态因子分析:在COVID-19研究中对代谢物测量的应用

Jiachen Cai1, Robert J B Goudie1, Brian D M Tom1

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Statistics in medicine
|March 17, 2026
PubMed
概括

本研究引入了一个动态因子分析模型,从稀疏的代谢物数据中发现相互作用的生物途径. 该方法确定了与COVID-19严重程度和生物标志物 taurine 相关的 kynurenine 途径.

关键词:
根据COVID-19的情况,动态因素分析的动态因素分析纵向高维数据的纵向高维数据多输出高斯过程.稀少的数据稀少的数据.随机预期期望最大化最大化

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