论文标题
一种用于一般数据联合插补的灵活，有效的算法
A flexible and efficient algorithm for joint imputation of general data
论文作者
论文摘要
使用通用结构（例如，具有连续，二进制，无序的分类和序数变量的数据）通常使用完全条件规范（FCS）而不是关节建模进行。 FCS的一个关键缺点是，它不会调用适当的数据增强机制，并且不能保证所得的马尔可夫链蒙特卡洛程序的收敛性。使用联合建模的方法缺乏这些缺点，但尚未在通用结构数据中有效实施。我们通过开发一种新方法，即所谓的Gerbil算法来解决这些问题，该算法从潜在的关节多元正常模型中提取了基于一般结构化数据的潜在联合多元正常模型。该模型是使用一系列灵活条件线性模型构建的，该模型可以在实践中有效地在高维数据集上有效实现所得的过程。模拟表明，与利用FC的Gerbil相比，Gerbil的表现良好。此外，新方法相对于现有的FCS程序具有计算有效的效率。
Imputation of data with general structures (e.g., data with continuous, binary, unordered categorical, and ordinal variables) is commonly performed with fully conditional specification (FCS) instead of joint modeling. A key drawback of FCS is that it does not invoke an appropriate data augmentation mechanism and as such convergence of the resulting Markov chain Monte Carlo procedure is not assured. Methods that use joint modeling lack these drawbacks but have not been efficiently implemented in data of general structures. We address these issues by developing a new method, the so-called GERBIL algorithm, that draws imputations from a latent joint multivariate normal model that underpins the generally structured data. This model is constructed using a sequence of flexible conditional linear models that enables the resulting procedure to be efficiently implemented on high dimensional datasets in practice. Simulations show that GERBIL performs well when compared to those that utilize FCS. Furthermore, the new method is computationally efficient relative to existing FCS procedures.
