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Updated: Jul 16, 2025

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

Published on: July 3, 2020

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用集成嵌套拉普拉斯近似来处理缺失数据和测量误差的联合贝叶斯框架

Emma Skarstein1, Sara Martino1, Stefanie Muff1,2

  • 1Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.

Biometrical journal. Biometrische Zeitschrift
|September 22, 2023
PubMed
概括

本研究引入了一个统一的贝叶斯框架,同时解决测量误差 (ME) 和回归共变量中缺失的数据. 该方法利用集成嵌套拉普拉斯近似 (INLA) 进行可靠的数据分析.

关键词:
贝叶斯联合模型是贝叶斯联合模型.伯克森测量误差 测量误差经典的测量误差是传统的测量误差.集成嵌套拉普拉斯近似方法缺失的数据 缺失的数据

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