IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v72y2014icp92-104.html
   My bibliography  Save this article

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.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    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).
    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. 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.
    2. Steven D. Silver, 2018. "Multivariate methodology for discriminating market segments in urban commuting," Public Transport, Springer, vol. 10(1), pages 63-89, May.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. Youngjoo Cho & Debashis Ghosh, 0. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 0, pages 1-39.
    10. Francesca Ghilotti & Ann-Sofie Pesonen & Sara E Raposo & Henric Winell & Olof Nyrén & Ylva Trolle Lagerros & Amelie Plymoth, 2018. "Physical activity, sleep and risk of respiratory infections: A Swedish cohort study," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-12, January.

    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. 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.
    3. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    4. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    5. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    6. Shankar Tumati & Huibert Burger & Sander Martens & Yvonne T van der Schouw & André Aleman, 2016. "Association between Cognition and Serum Insulin-Like Growth Factor-1 in Middle-Aged & Older Men: An 8 Year Follow-Up Study," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-12, April.
    7. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    8. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, Open Access Journal, vol. 11(3), pages 1-12, February.
    9. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    10. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    11. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    12. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    13. Debra Javeline & Tracy Kijewski-Correa & Angela Chesler, 2019. "Does it matter if you “believe” in climate change? Not for coastal home vulnerability," Climatic Change, Springer, vol. 155(4), pages 511-532, August.
    14. Christian König & Patrick Weigelt & Julian Schrader & Amanda Taylor & Jens Kattge & Holger Kreft, 2019. "Biodiversity data integration—the significance of data resolution and domain," PLOS Biology, Public Library of Science, vol. 17(3), pages 1-16, March.
    15. Hammon, Angelina & Zinn, Sabine, 2020. "Multiple imputation of binary multilevel missing not at random data," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 547-564.
    16. Maaz Gardezi & J. Gordon Arbuckle, 2019. "Spatially Representing Vulnerability to Extreme Rain Events Using Midwestern Farmers’ Objective and Perceived Attributes of Adaptive Capacity," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 17-34, January.
    17. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.
    18. Marisa A P Donnelly & Susanne Kluh & Robert E Snyder & Christopher M Barker, 2020. "Quantifying sociodemographic heterogeneities in the distribution of Aedes aegypti among California households," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(7), pages 1-21, July.
    19. André Blais & Eric Guntermann & Vincent Arel-Bundock & Ruth Dassonneville & Jean-François Laslier & Gabrielle Péloquin-Skulski, 2020. "Party Preference Representation," PSE Working Papers halshs-02946659, HAL.
    20. Natalia Estévez & Dominique Eich-Höchli & Michelle Dey & Gerhard Gmel & Joseph Studer & Meichun Mohler-Kuo, 2014. "Prevalence of and Associated Factors for Adult Attention Deficit Hyperactivity Disorder in Young Swiss Men," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-8, February.

    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.

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

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.