IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v106y2019i2p369-384..html
   My bibliography  Save this article

Pseudo-population bootstrap methods for imputed survey data

Author

Listed:
  • S Chen
  • D Haziza
  • C Léger
  • Z Mashreghi

Abstract

SummaryThe most common way to treat item nonresponse in surveys is to replace a missing value by a plausible value constructed on the basis of fully observed variables. Treating the imputed values as if they were observed may lead to invalid inferences. Bootstrap variance estimators for various finite population parameters are obtained using two pseudo-population bootstrap schemes. We establish the asymptotic properties of the resulting bootstrap variance estimators for population totals and population quantiles. A simulation study suggests that the methods perform well in terms of relative bias and coverage probability.

Suggested Citation

  • S Chen & D Haziza & C Léger & Z Mashreghi, 2019. "Pseudo-population bootstrap methods for imputed survey data," Biometrika, Biometrika Trust, vol. 106(2), pages 369-384.
    • Handle: RePEc:oup:biomet:v:106:y:2019:i:2:p:369-384.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asz001
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Conti, Pier Luigi & Mecatti, Fulvia & Nicolussi, Federica, 2022. "Efficient unequal probability resampling from finite populations," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    2. Bruch, Christian & Felderer, Barbara, 2024. "An Approximation of Joint Distributions of Weighting Variables Using a Pseudo Population Approach," OSF Preprints pg2wt, Center for Open Science.
    3. Zhonglei Wang & Liuhua Peng & Jae Kwang Kim, 2022. "Bootstrap inference for the finite population mean under complex sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1150-1174, September.
    4. Lili Yu & Yichuan Zhao, 2022. "A Bootstrap Method for a Multiple-Imputation Variance Estimator in Survey Sampling," Stats, MDPI, vol. 5(4), pages 1-11, November.
    5. Marius Stefan & Michael A. Hidiroglou, 2023. "A Bootstrap Variance Procedure for the Generalised Regression Estimator," International Statistical Review, International Statistical Institute, vol. 91(2), pages 294-317, August.

    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:oup:biomet:v:106:y:2019:i:2:p:369-384.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.