Conditional mixed models with crossed random effects
Conditional mixed models with crossed random effects
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DOI：
10.1348/000711006x110562
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发表时间：
2007-11-01
影响因子：
2.6
通讯作者：
de Boeck, Paul
中科院分区：
文献类型：
--
作者：
Tibaldi, Fabian S.;Verbeke, Geert;de Boeck, Paul
连续分层数据（如重复测量或荟萃分析数据）的分析可以通过线性混合效应模型进行。然而，在某些情况下，这个模型，在其标准形式，并提出计算问题。例如，在处理交叉随机效应模型时，如果每个交叉分类水平只有一个观测可用，则方差分量的估计将成为一项重要任务。伪随机的想法已被用于标准的广义线性多水平模型的二进制数据的上下文中。然而，即使在这种情况下，方差的估计问题仍然是重要的。本文首先借鉴条件线性混合效应模型理论，提出了一种拟合具有两个水平和连续产出的交叉随机效应模型的方法。我们还提出了一个交叉随机效应模型的二进制数据相结合的想法，条件Logistic回归与伪概率估计。我们将这种方法应用到一个案例研究中，数据来自心理测量学领域，并研究了一系列与参与者交叉的项目（响应）。模拟研究评估的操作特性的方法。
The analysis of continuous hierarchical data such as repeated measures or data from meta-analyses can be carried out by means of the linear mixed-effects model. However, in some situations this model, in its standard form, does pose computational problems. For example, when dealing with crossed random-effects models, the estimation of the variance components becomes a non-trivial task if only one observation is available for each cross-classified level. Pseudolikelihood ideas have been used in the context of binary data with standard generalized linear multilevel models. However, even in this case the problem of the estimation of the variance remains non-trivial. In this paper we first propose a method to fit a crossed random-effects model with two levels and continuous outcomes, borrowing ideas from conditional linear mixed-effects model theory. We also propose a crossed random-effects model for binary data combining ideas of conditional logistic regression with pseudolikelihood estimation. We apply this method to a case study with data coming from the field of psychometrics and study a series of items (responses) crossed with participants. A simulation study assesses the operational characteristics of the method.