A toolkit in SAS for the evaluation of multiple imputation methods
A toolkit in SAS for the evaluation of multiple imputation methods
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DOI：
10.1111/1467-9574.00219
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发表时间：
2003-02-01
影响因子：
1.5
通讯作者：
Gelsema, ES
中科院分区：
文献类型：
--
作者：
Brand, JPL;van Buuren, S;Gelsema, ES
本文概述了一种验证多重插补方法的策略。鲁宾的标准，适当的多重插补的出发点。我们描述了一种模拟方法，深入了解偏见和效率的插补过程的各个方面。我们提出了一种新的方法来创建不完整的数据下的一般随机缺失（MAR）机制。实施验证策略的软件可作为SAS/IML模块提供。该方法被应用于研究分类数据的多分类回归插补的行为。
This paper outlines a strategy to validate multiple imputation methods. Rubin's criteria for proper multiple imputation are the point of departure. We describe a simulation method that yields insight into various aspects of bias and efficiency of the imputation process. We propose a new method for creating incomplete data under a general Missing At Random (MAR) mechanism. Software implementing the validation strategy is available as a SAS/IML module. The method is applied to investigate the behavior of polytomous regression imputation for categorical data.