Woodard D B, Love T M T, Thurston S W, Ruppert D, Sathyanarayana S, Swan S H
School of Operations Research and Information Engineering, Cornell University, Ithaca, New York, U.S.A.
Biometrics. 2013 Sep;69(3):785-94. doi: 10.1111/biom.12037. Epub 2013 Jul 11.
We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious random effect multiple outcomes models and more general continuous latent factor models. We introduce a set of identifiable models along this spectrum that extend an existing random effect model for multiple outcomes nested in domains. We characterize the tradeoffs between parsimony and flexibility in this set of models, applying them to both simulated data and data relating sexually dimorphic traits in male infants to explanatory variables.
我们考虑用于多个相关结果的回归模型，其中这些结果嵌套于各个领域之中。我们表明，针对这种嵌套情况的随机效应模型可纳入标准因子模型框架，这使我们将建模选项视为一个连续区间，一端是简约的随机效应多结果模型，另一端是更通用的连续潜在因子模型。我们沿着这个连续区间引入了一组可识别的模型，这些模型扩展了现有的针对嵌套于领域中的多个结果的随机效应模型。我们刻画了这组模型在简约性和灵活性之间的权衡，并将它们应用于模拟数据以及将男婴的性二态性特征与解释变量相关联的数据。
