Multiple Imputation with Predictive Mean Matching Method for Numerical Missing Data

Emha Fathul Akmam, Titin Siswantining, Saskya Mary Soemartojo, Devvi Sarwinda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Missing data are condition when there are some missing values or empty entries on several observations on data. It could inhibit statistical analysis process and might give a bias conclusion from the analysis if couldn't be handled properly. This problem can be found on some linear regression analysis. One way to handle this problem is using multiple imputation (MI) method named Predictive Mean Matching (PMM). PMM will matching the predictive mean distance of incomplete observations with the complete observations. To get the multiple imputation concept, the predictive mean of incomplete observations were estimated by Bayesian approach while the complete observations were estimated with ordinary least square. Thus, the complete observation that has the closest distance will be a donor value for the incomplete one. Simulation data with two variable (x and y), univariate missing data pattern (on y), and MAR mechanism is used to analyzed the effectiveness of PMM based on relative efficiency estimation result of missing covariate data. Regression analysis used x as independent variable and y as dependent variable. The result showed that PMM give a significant coefficient regression parameter at 5% level of significance and only loss 1 % of relative efficiency.

Original languageEnglish
Title of host publicationICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences
Subtitle of host publicationAccelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728146102
DOIs
Publication statusPublished - Oct 2019
Event3rd International Conference on Informatics and Computational Sciences, ICICOS 2019 - Semarang, Indonesia
Duration: 29 Oct 201930 Oct 2019

Publication series

NameICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings

Conference

Conference3rd International Conference on Informatics and Computational Sciences, ICICOS 2019
CountryIndonesia
CitySemarang
Period29/10/1930/10/19

Keywords

  • linear regression analysis
  • missing values
  • multiple imputation
  • predictive mean matching

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