文章基本信息

标题：MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors
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作者：Juned Siddique ; Ofer Harel
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2009
卷号：29
期号：1
页码：1-18
语种：English
出版社：University of California, Los Angeles
摘要：In this paper we describe MIDAS: a SAS macro for multiple imputation using distance aided selection of donors which implements an iterative predictive mean matching hot-deck for imputing missing data. This is a flexible multiple imputation approach that can handle data in a variety of formats: continuous, ordinal, and scaled. Because the imputation models are implicit, it is not necessary to specify a parametric distribution for each variable to be imputed. MIDAS also allows the user to address the sensitivity of their inferences to different assumptions concerning the missing data mechanism. An example using MIDAS to impute missing data is presented and MIDAS is compared to existing missing data software.