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State of the Multiple Imputation Software

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  • Yucel, Recai M.

Abstract

Owing to its practicality as well as strong inferential properties, multiple imputation has been increasingly popular in the analysis of incomplete data. Methods that are not only computationally elegant but also applicable in wide spectrum of statistical incomplete data problems have also been increasingly implemented in a numerous computing environments. Unfortunately, however, the speed of this development has not been replicated in reaching to "sophisticated" users. While the researchers have been quite successful in developing the underlying software, documentation in a style that would be most reachable to the greater scientific society has been lacking. The main goal of this special volume is to close this gap by articles that illustrate these software developments. Here I provide a brief history of multiple imputation and relevant software and highlight the contents of the contributions. Potential directions for the future of the software development is also provided.

Suggested Citation

  • Yucel, Recai M., 2011. "State of the Multiple Imputation Software," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i01).
    • Handle: RePEc:jss:jstsof:v:045:i01
    DOI: http://hdl.handle.net/10.18637/jss.v045.i01
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    Cited by:

    1. Celeste Combrinck & Vanessa Scherman & David Maree & Sarah Howie, 2018. "Multiple Imputation for Dichotomous MNAR Items Using Recursive Structural Equation Modeling With Rasch Measures as Predictors," SAGE Open, , vol. 8(1), pages 21582440187, February.
    2. Shakir Hussain & Mohamed A Mohammed & Ghazi Shukur, 2013. "Congenial Multiple Imputation and Matched Pairs Models for Square Tables: An Example of patients¡¯ self-management," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 2(1), pages 1-8, April.
    3. Josse, Julie & Husson, François, 2016. "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i01).
    4. Schalk Burger & Searle Silverman & Gary van Vuuren, 2018. "Deriving Correlation Matrices for Missing Financial Time-Series Data," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 105-105, October.
    5. Yajuan Si & Roderick J. A. Little & Ya Mo & Nell Sedransk, 2023. "A Case Study of Nonresponse Bias Analysis in Educational Assessment Surveys," Journal of Educational and Behavioral Statistics, , vol. 48(3), pages 271-295, June.
    6. Eisele, Martin & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," EconStor Preprints 100007, ZBW - Leibniz Information Centre for Economics.
    7. Rüdiger Mutz & Lutz Bornmann & Hans-Dieter Daniel, 2015. "Testing for the fairness and predictive validity of research funding decisions: A multilevel multiple imputation for missing data approach using ex-ante and ex-post peer evaluation data from the Austr," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2321-2339, November.
    8. Florian M. Hollenbach & Iavor Bojinov & Shahryar Minhas & Nils W. Metternich & Michael D. Ward & Alexander Volfovsky, 2021. "Multiple Imputation Using Gaussian Copulas," Sociological Methods & Research, , vol. 50(3), pages 1259-1283, August.

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