Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Juned Siddique, Ofer Harel
Title: MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors
Abstract: 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.

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Paper: MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors     Download PDF (Downloads: 5976)
Supplements:
MIDAS.sas.zip: MIDAS.sas: SAS source code Download (Downloads: 1092; 4KB)
v29i09.sas.zip: v29i09.sas: SAS example code from the paper Download (Downloads: 992; 2KB)
v29i09-kid-data.sas.zip: v29i09-kid-data.sas: SAS code for KID data preparation Download (Downloads: 940; 5KB)
v29i09-kid-imputation.sas.zip: v29i09-kid-imputatION.sas: SAS code for KID imputation usig MIDAS Download (Downloads: 952; 1KB)
DOI: 10.18637/jss.v029.i09

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.