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Updated: Jan 18, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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在双重交叉拟合的目标最大概率估计器中找到最佳数量的分割和重复

Mohammad Ehsanul Karim1,2, Momenul Haque Mondol1,3

  • 1School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.

Pharmaceutical statistics
|September 11, 2025
PubMed
概括

对于像TMLE这样的双稳定方法,在双交叉拟合 (DCF) 中使用三到五个数据分割优化了统计性能. 超过25次重复并不能改善结果,建议使用完整的数据来估计麻烦.

关键词:
TMLE TMLE TMLE TMLE TMLE TMLE TMLE TMLE TMLE有关因果推理的推理.交叉适应 交叉适应 交叉适应双重强大的强大的强大.机器学习是机器学习.样本的分割 样本的分割

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