IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v72y2014icp92-104.html

Recursive partitioning for missing data imputation in the presence of interaction effects

Author

Listed:
  • Doove, L.L.
  • Van Buuren, S.
  • Dusseldorp, E.

Abstract

Standard approaches to implement multiple imputation do not automatically incorporate nonlinear relations like interaction effects. This leads to biased parameter estimates when interactions are present in a dataset. With the aim of providing an imputation method which preserves interactions in the data automatically, the use of recursive partitioning as imputation method is examined. Three recursive partitioning techniques are implemented in the multiple imputation by chained equations framework. It is investigated, using simulated data, whether recursive partitioning creates appropriate variability between imputations and unbiased parameter estimates with appropriate confidence intervals. It is concluded that, when interaction effects are present in a dataset, substantial gains are possible by using recursive partitioning for imputation compared to standard applications. In addition, it is shown that the potential of recursive partitioning imputation approaches depends on the relevance of a possible interaction effect, the correlation structure of the data, and the type of possible interaction effect present in the data.

Suggested Citation

  • Doove, L.L. & Van Buuren, S. & Dusseldorp, E., 2014. "Recursive partitioning for missing data imputation in the presence of interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 92-104.
    • Handle: RePEc:eee:csdana:v:72:y:2014:i:c:p:92-104
    DOI: 10.1016/j.csda.2013.10.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947313003939
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2013.10.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Iacus, Stefano M. & Porro, Giuseppe, 2007. "Missing data imputation, matching and other applications of random recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 773-789, October.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    4. Iacus, Stefano & Porro, Giuseppe, 2008. "Invariant and Metric Free Proximities for Data Matching: An R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i11).
    5. Nonyane Bareng A. S. & Foulkes Andrea S., 2007. "Multiple Imputation and Random Forests (MIRF) for Unobservable, High-Dimensional Data," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-19, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Simonse, Olaf & Van Dijk, Wilco W. & Van Dillen, Lotte F. & Van Dijk, Eric, 2024. "Economic predictors of the subjective experience of financial stress," Journal of Behavioral and Experimental Finance, Elsevier, vol. 42(C).
    2. Christian Aßmann & Ariane Würbach & Solange Goßmann & Ferdinand Geissler & Anika Bela, 2017. "Nonparametric Multiple Imputation for Questionnaires with Individual Skip Patterns and Constraints: The Case of Income Imputation in the National Educational Panel Study," Sociological Methods & Research, , vol. 46(4), pages 864-897, November.
    3. Vincent Audigier & Ndèye Niang, 2023. "Clustering with missing data: which equivalent for Rubin’s rules?," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 623-657, September.
    4. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    5. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    6. Hayes, Timothy & McArdle, John J., 2017. "Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 35-52.
    7. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    8. Kevin Emery & Matthias Studer & André Berchtold, 2025. "Comparison of imputation methods for univariate categorical longitudinal data," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1767-1791, April.
    9. Xiaofei Ma & Qiuyan Zhong, 2016. "Missing value imputation method for disaster decision-making using K nearest neighbor," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 767-781, March.
    10. Svetlana Zhuchkova & Aleksei Rotmistrov, 2022. "How to choose an approach to handling missing categorical data: (un)expected findings from a simulated statistical experiment," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(1), pages 1-22, February.
    11. Christian Stetter & Philipp Mennig & Johannes Sauer, 2022. "Using Machine Learning to Identify Heterogeneous Impacts of Agri-Environment Schemes in the EU: A Case Study," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 723-759.
    12. A. R. Linero, 2017. "Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness," Biometrika, Biometrika Trust, vol. 104(2), pages 327-341.
    13. Christian Aßmann & Jean-Christoph Gaasch & Doris Stingl, 2023. "A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1495-1528, December.
    14. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 33-58, December.
    15. Hapfelmeier, A. & Ulm, K., 2014. "Variable selection by Random Forests using data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 129-139.
    16. Steven D. Silver, 2018. "Multivariate methodology for discriminating market segments in urban commuting," Public Transport, Springer, vol. 10(1), pages 63-89, May.
    17. Michael Bergrab & Christian Aßmann, 2024. "Automated Bayesian variable selection methods for binary regression models with missing covariate data," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 18(2), pages 203-244, June.
    18. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    19. Oyebayo Ridwan Olaniran & Ali Rashash R. Alzahrani, 2025. "Bayesian Random Forest with Multiple Imputation by Chain Equations for High-Dimensional Missing Data: A Simulation Study," Mathematics, MDPI, vol. 13(6), pages 1-32, March.
    20. repec:plo:pone00:0190270 is not listed on IDEAS
    21. Christian Aßmann & Marcel Preising, 2020. "Bayesian estimation and model comparison for linear dynamic panel models with missing values," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 536-557, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    2. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 33-58, December.
    3. Lee, Yongseok & Leite, Walter, 2025. "Enhancing Propensity Score Analysis with data Missing Not at Random: Introducing Dual-Forest Proximity Imputation," OSF Preprints ex8ad_v1, Center for Open Science.
    4. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    5. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    6. Gerko Vink & Stef van Buuren, 2013. "Multiple Imputation of Squared Terms," Sociological Methods & Research, , vol. 42(4), pages 598-607, November.
    7. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    8. David Kaplan & Jianshen Chen, 2012. "A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 581-609, July.
    9. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    10. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    11. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    12. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    13. Lin Lin & Rachel L Spreng & Kelly E Seaton & S Moses Dennison & Lindsay C Dahora & Daniel J Schuster & Sheetal Sawant & Peter B Gilbert & Youyi Fong & Neville Kisalu & Andrew J Pollard & Georgia D Tom, 2024. "GeM-LR: Discovering predictive biomarkers for small datasets in vaccine studies," PLOS Computational Biology, Public Library of Science, vol. 20(11), pages 1-23, November.
    14. Kim, Ahhyoun & Kim, Hyunjoong, 2022. "A new classification tree method with interaction detection capability," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    15. Hayk Manucharyan, 2020. "How do managers actually choose suppliers? Evidence from revealed preference data," Working Papers 2020-12, Faculty of Economic Sciences, University of Warsaw.
    16. Aleix Alcacer & Irene Epifanio & Jorge Valero & Alfredo Ballester, 2021. "Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size," Mathematics, MDPI, vol. 9(7), pages 1-15, April.
    17. Omerašević Amela & Selimović Jasmina, 2020. "Classification Ratemaking Using Decision Tree in the Insurance Market of Bosnia and Herzegovina," South East European Journal of Economics and Business, Sciendo, vol. 15(2), pages 124-139, December.
    18. Ghosh, Atish R. & Qureshi, Mahvash S. & Kim, Jun Il & Zalduendo, Juan, 2014. "Surges," Journal of International Economics, Elsevier, vol. 92(2), pages 266-285.
      • Mahvash S Qureshi & Mr. Atish R. Ghosh & Mr. Juan Zalduendo & Mr. Jun I Kim, 2012. "Surges," IMF Working Papers 2012/022, International Monetary Fund.
    19. Rapp, Hannah & Fredrick, Stephanie & Nickerson, Amanda, 2025. "Cyber victimization reports between parents and children: an examination of agreement predictors," Children and Youth Services Review, Elsevier, vol. 177(C).
    20. Huang Lin & Merete Eggesbø & Shyamal Das Peddada, 2022. "Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data," Nature Communications, Nature, vol. 13(1), pages 1-16, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:72:y:2014:i:c:p:92-104. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.