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Updated: May 7, 2025

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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用拉普拉斯近似方法对具有模式共变矩阵的等级通用线性混合模型进行边际推理.

Jay M Ver Hoef1, Eryn Blagg2, Michael Dumelle3

  • 1Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, Washington, USA.

Environmetrics
|December 31, 2024
PubMed
概括

我们为具有复杂共变性结构的通用线性混合模型提供了一种快速,全参数的方法. 这种方法使完全的边际推断和预测成为可能,优于贝叶斯方法,并提供比INLA更大的灵活性.

关键词:
拉普拉斯的近似方法一般化的线性混合模型.这是边缘化,边缘化.一个有模式的协差矩阵.空间统计的空间统计.时间序列时间序列

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