Score-Guided Structural Equation Model Trees.
Score-Guided Structural Equation Model Trees.
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DOI：
10.3389/fpsyg.2020.564403
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发表时间：
2020
影响因子：
3.8
通讯作者：
Brandmaier AM
中科院分区：
文献类型：
--
作者：
Arnold M;Voelkle MC;Brandmaier AM
结构方程模型 (SEM) 树是数据驱动的工具，用于查找预测 SEM 参数组差异的变量。 SEM 树建立在决策树范例的基础上，通过生长树结构将数据集递归地划分为同构子集。在过去的研究中，SEM 树主要使用 R 包 semtree 进行估计。 semtree 包中的原始算法通过计算每个协变量的每个可能分割的似然比来选择协变量之间的分割变量。获得这些似然比的计算要求很高。作为一种补救措施，我们建议通过最近在心理测量学中流行的一系列基于分数的测试来指导 SEM 树的构建。这些基于分数的测试监视似然函数的个案导数的波动，以检测组之间的参数差异。与似然比方法相比，基于分数的测试在计算上非常高效，因为它们不需要为每个可能的分割重新拟合模型。在本文中，我们引入了评分引导的 SEM 树，在 semtree 中实现它们，并通过蒙特卡洛模拟评估它们的性能。
Structural equation model (SEM) trees are data-driven tools for finding variables that predict group differences in SEM parameters. SEM trees build upon the decision tree paradigm by growing tree structures that divide a data set recursively into homogeneous subsets. In past research, SEM trees have been estimated predominantly with the R package semtree. The original algorithm in the semtree package selects split variables among covariates by calculating a likelihood ratio for each possible split of each covariate. Obtaining these likelihood ratios is computationally demanding. As a remedy, we propose to guide the construction of SEM trees by a family of score-based tests that have recently been popularized in psychometrics. These score-based tests monitor fluctuations in case-wise derivatives of the likelihood function to detect parameter differences between groups. Compared to the likelihood-ratio approach, score-based tests are computationally efficient because they do not require refitting the model for every possible split. In this paper, we introduce score-guided SEM trees, implement them in semtree, and evaluate their performance by means of a Monte Carlo simulation.
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影响因子：
7
作者：
Brandmaier AM;von Oertzen T;McArdle JJ;Lindenberger U
通讯作者：
Lindenberger U
DOI：
10.1080/10705511.2019.1667240
发表时间：
2019-11-04
影响因子：
6
作者：
Arnold, Manuel;Oberski, Daniel L.;Voelkle, Manuel C.
通讯作者：
Voelkle, Manuel C.
影响因子：
3.8
作者：
Hildebrandt, Andrea;Luedtke, Oliver;Wilhelm, Oliver
通讯作者：
Wilhelm, Oliver
影响因子：
7
作者：
Brandmaier, Andreas M.;Prindle, John J.;Lindenberger, Ulman
通讯作者：
Lindenberger, Ulman
影响因子：
5
作者：
Jedidi, K;Jagpal, HS;DESarbo, WS
通讯作者：
DESarbo, WS