MIDAS: A SAS macro for multiple imputation using distance-aided selection of donors

Juned Siddique*, Ofer Harel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1-18
Number of pages18
JournalJournal of Statistical Software
Volume29
Issue number9
DOIs
StatePublished - Jan 1 2009

Keywords

  • Abb
  • Approximate bayesian boot-strap
  • Hot-deck
  • Missing data
  • Nmar
  • Nonignorable
  • Not missing at random
  • Predictive mean matching

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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