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Multiple Imputation: an attempt to retell the evolutionary process

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  • Florian Meinfelder

Abstract

Multiple Imputation describes a strategy for analyzing incomplete data that accounts for uncertainty in the missing data by replacing (imputing) each missing value by several ‘candidates’. The actual implementation of any Multiple Imputation method is typically computationally expensive which is why the concept has only really caught on around the verge of the new millennium, when the first algorithms for Multiple Imputation had become accessible. In this article, we are going to give a rough overview of the shortcomings of methods for handling missing data prior to Rubin’s work in the late 1970s, and we explore the conceptual innovations that might have lead to Multiple Imputation based on an example, where mean imputation is the steppingstone for more advanced methods. The general concept of Multiple Imputation is explained using a simulated trivariate data set, and the imputation model is based on the standard Bayesian linear model, in order to explain the method as illustrative as possible. Copyright The Author(s) 2014

Suggested Citation

  • Florian Meinfelder, 2014. "Multiple Imputation: an attempt to retell the evolutionary process," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(4), pages 249-267, November.
    • Handle: RePEc:spr:astaws:v:8:y:2014:i:4:p:249-267
    DOI: 10.1007/s11943-014-0151-8
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    References listed on IDEAS

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    1. Donald B. Rubin, 2003. "Nested multiple imputation of NMES via partially incompatible MCMC," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 3-18, February.
    2. Scheuren, Fritz, 2005. "Multiple Imputation: How It Began and Continues," The American Statistician, American Statistical Association, vol. 59, pages 315-319, November.
    3. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
    4. Su, Yu-Sung & Gelman, Andrew & Hill, Jennifer & Yajima, Masanao, 2011. "Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i02).
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    Cited by:

    1. Ralf Thomas Münnich, 2015. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(3), pages 167-171, December.
    2. Göran Kauermann & Michael Windmann & Ralf Münnich, 2020. "Datenerhebung bei Mietspiegeln: Überblick und Einordnung aus Sicht der Statistik [Collection of data for rent indexes: Overview and discussion from a statistical perspective]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(2), pages 145-162, July.
    3. Ralf Münnich & Siegfried Gabler & Christian Bruch & Jan Pablo Burgard & Tobias Enderle & Jan-Philipp Kolb & Thomas Zimmermann, 2015. "Tabellenauswertungen im Zensus unter Berücksichtigung fehlender Werte," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(3), pages 269-304, December.
    4. Ralf Thomas Münnich, 2016. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(4), pages 197-203, December.
    5. Ralf Thomas Münnich, 2014. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(4), pages 179-181, November.
    6. Sandra Krapf & Michael Wagner, 2020. "Housing Affordability, Housing Tenure Status and Household Density: Are Housing Characteristics Associated with Union Dissolution?," European Journal of Population, Springer;European Association for Population Studies, vol. 36(4), pages 735-764, September.

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    Keywords

    Missing data; Multiple Imputation;

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