IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2008i2p405-415.html
   My bibliography  Save this article

Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data

Author

Listed:
  • Siddique, Juned
  • Belin, Thomas R.

Abstract

An Approximate Bayesian Bootstrap (ABB) offers advantages in incorporating appropriate uncertainty when imputing missing data, but most implementations of the ABB have lacked the ability to handle nonignorable missing data where the probability of missingness depends on unobserved values. This paper outlines a strategy for using an ABB to multiply impute nonignorable missing data. The method allows the user to draw inferences and perform sensitivity analyses when the missing data mechanism cannot automatically be assumed to be ignorable. Results from imputing missing values in a longitudinal depression treatment trial as well as a simulation study are presented to demonstrate the method's performance. We show that a procedure that uses a different type of ABB for each imputed data set accounts for appropriate uncertainty and provides nominal coverage.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:2:p:405-415
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00391-5
    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. Demirtas, Hakan & Arguelles, Lester M. & Chung, Hwan & Hedeker, Donald, 2007. "On the performance of bias-reduction techniques for variance estimation in approximate Bayesian bootstrap imputation," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 4064-4068, May.
    2. 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.
    3. Schenker, Nathaniel & Taylor, Jeremy M. G., 1996. "Partially parametric techniques for multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 22(4), pages 425-446, 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. Tian Li & Julian M. Somers & Xiaoqiong J. Hu & Lawrence C. McCandless, 2019. "Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 184-205, April.
    2. 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.
    3. Vijayan K. Pillai & Fang-Hsun Wei & Arati Maleku, 2013. "International Non-Governmental Organizations in Latin America and Social Capital," SAGE Open, , vol. 3(4), pages 21582440135, December.
    4. Bailey, Michael & Hopkins, Daniel J. & Rogers, Todd, 2013. "Unresponsive and Unpersuaded: The Unintended Consequences of Voter Persuasion Efforts," Working Paper Series rwp13-034, Harvard University, John F. Kennedy School of Government.
    5. Ferrari, Pier Alda & Annoni, Paola & Barbiero, Alessandro & Manzi, Giancarlo, 2011. "An imputation method for categorical variables with application to nonlinear principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2410-2420, July.

    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. repec:jss:jstsof:29:i09 is not listed on IDEAS
    2. Jonathan Hambur & Gianni La Cava, 2018. "Do Interest Rates Affect Business Investment? Evidence from Australian Company-level Data," RBA Research Discussion Papers rdp2018-05, Reserve Bank of Australia.
    3. Fumagalli, Laura & Sala, Emanuela, 2011. "The total survey error paradigm and pre-election polls: the case of the 2006 Italian general elections," ISER Working Paper Series 2011-29, Institute for Social and Economic Research.
    4. Siddique, Juned & Harel, Ofer, 2009. "MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i09).
    5. 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.
    6. 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.
    7. Robert J. Batt & Christian Terwiesch, 2015. "Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department," Management Science, INFORMS, vol. 61(1), pages 39-59, January.
    8. Gianluca Gazzola & Myong K. Jeong, 2021. "Support vector regression for polyhedral and missing data," Annals of Operations Research, Springer, vol. 303(1), pages 483-506, August.
    9. Kristian Kleinke & Jost Reinecke, 2013. "Multiple imputation of incomplete zero-inflated count data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 311-336, August.
    10. Patrick M. Joyce & Donald Malec & Roderick J. A. Little & Aaron Gilary & Alfredo Navarro & Mark E. Asiala, 2014. "Statistical Modeling Methodology for the Voting Rights Act Section 203 Language Assistance Determinations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 36-47, March.
    11. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.
    12. Grabka, Markus & Westermeier, Christian, 2014. "Estimating the Impact of Alternative Multiple Imputation Methods on Longitudinal Wealth Data," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100353, Verein für Socialpolitik / German Economic Association.
    13. 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.
    14. 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.
    15. Timm Bönke & Markus M. Grabka & Carsten Schröder & Edward N. Wolff & Lennard Zyska, 2019. "The Joint Distribution of Net Worth and Pension Wealth in Germany," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(4), pages 834-871, December.
    16. 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.
    17. Moritz Kuhn & Moritz Schularick & Ulrike I. Steins, 2020. "Income and Wealth Inequality in America, 1949–2016," Journal of Political Economy, University of Chicago Press, vol. 128(9), pages 3469-3519.
    18. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    19. Hendrik Jürges & Lars Thiel & Tabea Bucher-Koenen & Johannes Rausch & Morten Schuth & Axel Börsch-Supan, 2014. "Health, Financial Incentives, and Early Retirement: Microsimulation Evidence for Germany," NBER Chapters, in: Social Security Programs and Retirement Around the World: Disability Insurance Programs and Retirement, pages 285-330, National Bureau of Economic Research, Inc.
    20. 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.
    21. Alina K. Bartscher & Moritz Kuhn & Moritz Schularick, 2020. "The College Wealth Divide: Education and Inequality in America, 1956-2016," Review, Federal Reserve Bank of St. Louis, vol. 102(1), pages 19-49.

    More about this item

    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:53:y:2008:i:2:p:405-415. 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.