MIDAS : A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors

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: conti...

Full description

Saved in:
Bibliographic Details
Published inJournal of statistical software Vol. 29; no. 9
Main Authors Siddique, Juned, Harel, Ofer
Format Journal Article
LanguageEnglish
Published Foundation for Open Access Statistics 2009
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:1548-7660
1548-7660
DOI:10.18637/jss.v029.i09