IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v82y2015icp173-185.html

A hot deck imputation procedure for multiply imputing nonignorable missing data: The proxy pattern-mixture hot deck

Author

Listed:
  • Sullivan, Danielle
  • Andridge, Rebecca

Abstract

Hot deck imputation is a common method for handling item nonresponse in surveys, but most implementations assume data are missing at random (MAR). A new hot deck method for imputation of a continuous partially missing outcome variable that harnesses the power of available covariates but does not assume data are MAR is proposed. A parametric model is used to create predicted means for both donors and donees under varying assumptions on the missing data mechanism, ranging from MAR to missing not at random (MNAR). For a given assumption on the missingness mechanism, the predicted means are used to define distances between donors and donees and probabilities of selection proportional to those distances. Multiple imputation using the hot deck is performed to create a set of completed data sets, using an approximate Bayesian bootstrap to ensure “proper” imputations. This new hot deck method creates an intuitive sensitivity analysis where imputations may be performed under MAR and under varying MNAR mechanisms, and the resulting impact on inference can be evaluated. In addition, a donor quality metric is proposed to help identify situations where close matches of donor to donee are not available, which can occur under strong MNAR assumptions. Bias and coverage of estimates from the proposed method are investigated through simulation and the method is applied to estimation of income in the Ohio Medicaid Assessment Survey. Results show that the method performs best when covariates are at least moderately predictive of the partially missing outcome, and without such covariates it effectively reduces to a simple random hot deck for all missingness assumptions.

Suggested Citation

  • Sullivan, Danielle & Andridge, Rebecca, 2015. "A hot deck imputation procedure for multiply imputing nonignorable missing data: The proxy pattern-mixture hot deck," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 173-185.
    • Handle: RePEc:eee:csdana:v:82:y:2015:i:c:p:173-185
    DOI: 10.1016/j.csda.2014.09.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947314002692
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2014.09.008?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. Jae Kwang Kim & J. Michael Brick & Wayne A. Fuller & Graham Kalton, 2006. "On the bias of the multiple‐imputation variance estimator in survey sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 509-521, June.
    2. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    3. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    4. Siddique, Juned & Belin, Thomas R., 2008. "Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 405-415, December.
    5. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    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. Nancy, Jane Y. & Khanna, Nehemiah H. & Arputharaj, Kannan, 2017. "Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 63-79.
    2. Andridge Rebecca R. & Little Roderick J.A., 2020. "Proxy Pattern-Mixture Analysis for a Binary Variable Subject to Nonresponse," Journal of Official Statistics, Sciendo, vol. 36(3), pages 703-728, September.

    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. Chenyang Gu & Roee Gutman, 2017. "Combining item response theory with multiple imputation to equate health assessment questionnaires," Biometrics, The International Biometric Society, vol. 73(3), pages 990-998, September.
    2. Chia-Ning Wang & Roderick Little & Bin Nan & Siobán D. Harlow, 2011. "A Hot-Deck Multiple Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories," Biometrics, The International Biometric Society, vol. 67(4), pages 1573-1582, December.
    3. Adel Bosch & Steven F. Koch, 2021. "Individual and Household Debt: Does Imputation Choice Matter?," Working Papers 202141, University of Pretoria, Department of Economics.
    4. Yanqing Sun & Li Qi & Fei Heng & Peter B. Gilbert, 2020. "A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 791-814, August.
    5. 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.
    6. Jana Emmenegger & Ralf Münnich & Jannik Schaller, 2022. "Evaluating Data Fusion Methods to Improve Income Modelling," Research Papers in Economics 2022-03, University of Trier, Department of Economics.
    7. Anika Rasner & Joachim R. Frick & Markus M. Grabka, 2013. "Statistical Matching of Administrative and Survey Data," Sociological Methods & Research, , vol. 42(2), pages 192-224, May.
    8. Kristian Kleinke, 2017. "Multiple Imputation Under Violated Distributional Assumptions: A Systematic Evaluation of the Assumed Robustness of Predictive Mean Matching," Journal of Educational and Behavioral Statistics, , vol. 42(4), pages 371-404, August.
    9. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    10. Shu Yang & Jae Kwang Kim, 2020. "Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 839-861, September.
    11. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
    12. Gerko Vink & Stef van Buuren, 2013. "Multiple Imputation of Squared Terms," Sociological Methods & Research, , vol. 42(4), pages 598-607, November.
    13. Valaei, Naser & Rezaei, Sajad & Ismail, Wan Khairuzzaman Wan, 2017. "Examining learning strategies, creativity, and innovation at SMEs using fuzzy set Qualitative Comparative Analysis and PLS path modeling," Journal of Business Research, Elsevier, vol. 70(C), pages 224-233.
    14. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    15. Renate S M Buisman & Katharina Pittner & Marieke S Tollenaar & Jolanda Lindenberg & Lisa J M van den Berg & Laura H C G Compier-de Block & Joost R van Ginkel & Lenneke R A Alink & Marian J Bakermans-K, 2020. "Intergenerational transmission of child maltreatment using a multi-informant multi-generation family design," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-23, March.
    16. Rasner, Anika & Frick, Joachim R. & Grabka, Markus M., 2013. "Statistical Matching of Administrative and Survey Data: An Application to Wealth Inequality Analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 42(2), pages 192-224.
    17. 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.
    18. Marco Di Zio & Ugo Guarnera & Roberta Varriale, 2016. "Estimation of the main variables of the economic account of small and medium enterprises based on administrative sources," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 18(1), pages 71-81.
    19. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani, 2016. "Dirty spatial econometrics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 177-189, January.
    20. 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.

    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:82:y:2015:i:c:p:173-185. 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.