Random-covariances and mixed-effects models for imputing multivariate multilevel continuous data.
Random-covariances and mixed-effects models for imputing multivariate multilevel continuous data.
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DOI：
10.1177/1471082x1001100404
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发表时间：
2011-08
影响因子：
1
通讯作者：
Yucel RM
中科院分区：
文献类型：
--
作者：
Yucel RM
不完全数据问题的原则性技术日益成为主流统计实践的一部分。在目前提出的许多技术中，多重插值推理（MI）已成为最受欢迎的技术之一。虽然在横截面设置中可以使用许多导致MI推理的策略，但在多层应用中不存在相同的丰富度。可用于多层应用的有限方法依赖于混合效应模型的多变量适应。该方法保留了聚类间的均值结构，并将不同的方差成分纳入到插值过程中。在本文中，我通过考虑随机协方差结构来增加这些方法并开发计算算法。这种新的输入建模策略的吸引力在于正确地反映了数据联合分布的均值和方差结构，并允许不同聚类之间的协方差不同。使用马尔可夫链蒙特卡罗技术，在给定观测数据的情况下，模拟缺失数据的预测分布，从而创建多个imputations。为了规避支持1级误差项的独立协方差估计的大样本量要求，我考虑了模拟随机效应分布的先验分配。这些技术在一个探讨受害与个人和环境层面因素之间关系的例子中得到说明，这些因素会增加暴力犯罪的风险。
Principled techniques for incomplete-data problems are increasingly part of mainstream statistical practice. Among many proposed techniques so far, inference by multiple imputation (MI) has emerged as one of the most popular. While many strategies leading to inference by MI are available in cross-sectional settings, the same richness does not exist in multilevel applications. The limited methods available for multilevel applications rely on the multivariate adaptations of mixed-effects models. This approach preserves the mean structure across clusters and incorporates distinct variance components into the imputation process. In this paper, I add to these methods by considering a random covariance structure and develop computational algorithms. The attraction of this new imputation modeling strategy is to correctly reflect the mean and variance structure of the joint distribution of the data, and allow the covariances differ across the clusters. Using Markov Chain Monte Carlo techniques, a predictive distribution of missing data given observed data is simulated leading to creation of multiple imputations. To circumvent the large sample size requirement to support independent covariance estimates for the level-1 error term, I consider distributional impositions mimicking random-effects distributions assigned a priori. These techniques are illustrated in an example exploring relationships between victimization and individual and contextual level factors that raise the risk of violent crime.