IDEAS home Printed from https://ideas.repec.org/a/taf/mpopst/v29y2022i4p204-225.html
   My bibliography  Save this article

Imputation for estimating the population mean in the presence of nonresponse, with application to fine particle density in Bangkok

Author

Listed:
  • Kanisa Chodjuntug
  • Nuanpan Lawson

Abstract

Air pollution in Bangkok, Thailand, is mainly due to fine particles emitted in exhaust gases. However, many data on fine particle concentrations are missing, a fact which may bias the statistics. Exponential-type imputation minimizing the mean square error allows for estimating the missing values of these concentrations and provides an estimate with smaller mean square error of the mean concentration levels. The bias and mean square error of the proposed estimator are calculated. Simulation shows that the relative efficiency is 5% higher up to 50 observations, 12% higher for 100 observations, and 25% higher for 200 observations. Application to the measurement of fine particle concentration in Bangkok yields a mean square error of 0.73 micrograms per cubic meter squared, for a mean level of 47.40 micrograms per cubic meter, while the mean square error by the best alternative estimator selected is 0.90 micrograms per cubic meter squared, for a mean level of 48.20 micrograms per cubic meter.

Suggested Citation

  • Kanisa Chodjuntug & Nuanpan Lawson, 2022. "Imputation for estimating the population mean in the presence of nonresponse, with application to fine particle density in Bangkok," Mathematical Population Studies, Taylor & Francis Journals, vol. 29(4), pages 204-225, October.
    • Handle: RePEc:taf:mpopst:v:29:y:2022:i:4:p:204-225
    DOI: 10.1080/08898480.2021.1997466
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/08898480.2021.1997466
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/08898480.2021.1997466?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 search for a different version of it.

    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:taf:mpopst:v:29:y:2022:i:4:p:204-225. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GMPS20 .

    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.