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Updated: Jun 13, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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功能主要组件分析作为混合效应模型的替代方案,用于描述缺失数据存在时稀疏重复的测量

Corentin Ségalas1, Catherine Helmer2, Robin Genuer1

  • 1Univ. Bordeaux, INSERM, INRIA, BPH, U1219, Bordeaux, France.

Statistics in medicine
|September 9, 2024
PubMed
概括

功能主要组件分析 (FPCA) 有效地分析稀疏的纵向健康数据,在随机缺失的数据方面表现良好. 然而,它与系统或缺失的非随机数据扎,类似于混合效应模型.

关键词:
功能性主要组件分析分析缺失的数据 缺失的数据混合型号混合型号的模型功能数据很少,功能数据很少.

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