A Comparison of Imputation Methods for Bayesian Factor Analysis Models
A Comparison of Imputation Methods for Bayesian Factor Analysis Models
复制标题
贝叶斯因子分析模型插补方法的比较
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Edgar C. Merkle
中科院分区：
文献类型：
--
作者：
Edgar C. Merkle
填补方法是心理学中处理缺失数据的常用方法。这些方法通常包括根据观察到的数据预测缺失数据，产生一个完整的数据集，便于标准统计分析。在贝叶斯因子分析的背景下，本文比较了非限制性多元正态模型（多重插补[MI]）下的插补和感兴趣的统计模型（数据增强[DA]）下的插补。前一种方法在应用研究中很流行，但从贝叶斯的角度来看，后一种方法更直接。模拟结果表明，DA产生较小的偏差的参数估计中等样本量和高缺失比例。然而，MI产生偏差较小的参数估计的大样本量与错误指定的模型。辅助变量在DA中的纳入也解决了，并提供了BUGS代码。
Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted multivariate normal model (Multiple Imputation [MI]) to imputation under the statistical model of interest (Data Augmentation [DA]). The former method is popular in applied research, but the latter method is more straightforward from a Bayesian perspective. Simulations demonstrate that DA yields less-biased parameter estimates for moderate sample sizes and high missingness proportions. MI, however, yields less-biased parameter estimates for large sample sizes with misspecified models. The incorporation of auxiliary variables in DA is also addressed, and BUGS code is provided.
影响因子：
7
作者：
L. Collins;J. Schafer;Chi-Ming Kam
通讯作者：
L. Collins;J. Schafer;Chi-Ming Kam
影响因子：
7
作者：
Lanza, ST;Collins, LM;Flaherty, BP
通讯作者：
Flaherty, BP