IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v45y2018i4p962-984.html
   My bibliography  Save this article

Semiparametric maximum likelihood inference for nonignorable nonresponse with callbacks

Author

Listed:
  • Zhong Guan
  • Denis H. Y. Leung
  • Jing Qin

Abstract

We model the nonresponse probabilities as logistic functions of the outcome variable and other covariates in the survey study with callback. The identification aspect of this callback model is investigated. Semiparametric maximum likelihood estimators of the parameters in the response probabilities are proposed and studied. As a result, an efficient estimator of the mean of the outcome variable is constructed using the estimated response probabilities. Moreover, if a regression model for the conditional mean of the outcome variable given some covariate is available, then we can obtain an even more efficient estimate of the mean of the outcome variable by fitting the regression model using an adjusted least‐squares method based on the estimated underlying distributions of the observed values. Simulation results show that the proposed method is more efficient compared with some existing competitors. The method is applied to data from the Singapore Life Panel, a survey of health spending using a population‐based sample of individuals aged 50–70 years, where nonresponse may be related to health.

Suggested Citation

  • Zhong Guan & Denis H. Y. Leung & Jing Qin, 2018. "Semiparametric maximum likelihood inference for nonignorable nonresponse with callbacks," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(4), pages 962-984, December.
    • Handle: RePEc:bla:scjsta:v:45:y:2018:i:4:p:962-984
    DOI: 10.1111/sjos.12330
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/sjos.12330
    Download Restriction: no
    File URL: https://libkey.io/10.1111/sjos.12330?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
    ---><---

    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:bla:scjsta:v:45:y:2018:i:4:p:962-984. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.