IDEAS home Printed from https://ideas.repec.org/h/eme/aecozz/s0731-905320190000039012.html
   My bibliography  Save this book chapter

Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling

In: The Econometrics of Complex Survey Data

Author

Listed:
  • Shu Yang
  • Jae Kwang Kim

Abstract

Nearest neighbor imputation has a long tradition for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the nearest neighbor imputation estimator for general population parameters, including population means, proportions and quantiles. For variance estimation, we propose novel replication variance estimation, which is asymptotically valid and straightforward to implement. The main idea is to construct replicates of the estimator directly based on its asymptotically linear terms, instead of individual records of variables. The simulation results show that nearest neighbor imputation and the proposed variance estimation provide valid inferences for general population parameters.

Suggested Citation

  • Shu Yang & Jae Kwang Kim, 2019. "Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling," Advances in Econometrics, in: The Econometrics of Complex Survey Data, volume 39, pages 209-234, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320190000039012
    DOI: 10.1108/S0731-905320190000039012
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320190000039012/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320190000039012/full/epub?utm_source=repec&utm_medium=feed&utm_campaign=repec&title=10.1108/S0731-905320190000039012
    Download Restriction: no
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320190000039012/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no
    File URL: https://libkey.io/10.1108/S0731-905320190000039012?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
    ---><---

    Citations

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


    Cited by:

    1. Chenyin Gao & Katherine Jenny Thompson & Jae Kwang Kim & Shu Yang, 2022. "Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1903-1930, October.
    2. 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.
    3. Maciej Berk{e}sewicz & Greta Bia{l}kowska & Krzysztof Marcinkowski & Magdalena Ma'slak & Piotr Opiela & Robert Pater & Katarzyna Zadroga, 2019. "Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration," Papers 1908.06731, arXiv.org.

    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:eme:aecozz:s0731-905320190000039012. 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: Emerald Support (email available below). General contact details of provider: .

    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.