Mixed-Effects Logistic Regression Models for Indirectly Observed Discrete Outcome Variables
Mixed-Effects Logistic Regression Models for Indirectly Observed Discrete Outcome Variables
复制标题
间接观察离散结果变量的混合效应 Logistic 回归模型
DOI：
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复制
发表时间：
2005
影响因子：
3.8
通讯作者：
J. Vermunt
中科院分区：
文献类型：
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作者：
J. Vermunt
一种完善的聚类数据建模方法在感兴趣的模型中引入了随机效应。当观测值相关时，混合效应逻辑回归模型可用于预测离散结果变量。提出了因变量为潜在类变量的混合效应logistic回归模型的扩展。该方法可以同时处理因变量的相关观测值和测量误差问题。如图所示，利用似然函数的特殊结构，采用具有E步长的EM算法进行最大似然估计是可行的。并以组织心理学为例对该模型进行了说明。
A well-established approach to modeling clustered data introduces random effects in the model of interest. Mixed-effects logistic regression models can be used to predict discrete outcome variables when observations are correlated. An extension of the mixed-effects logistic regression model is presented in which the dependent variable is a latent class variable. This method makes it possible to deal simultaneously with the problems of correlated observations and measurement error in the dependent variable. As is shown, maximum likelihood estimation is feasible by means of an EM algorithm with an E step that makes use of the special structure of the likelihood function. The new model is illustrated with an example from organizational psychology.