IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v107y2012i497p194-204.html
   My bibliography  Save this article

Multiple Imputation for M -Regression With Censored Covariates

Author

Listed:
  • Huixia Judy Wang
  • Xingdong Feng

Abstract

We develop a new multiple imputation approach for M -regression models with censored covariates. Instead of specifying parametric likelihoods, our method imputes the censored covariates by their conditional quantiles given the observed data, where the conditional quantiles are estimated through fitting a censored quantile regression process. The resulting estimator is shown to be consistent and asymptotically normal, and it improves the estimation efficiency by using information from cases with censored covariates. Compared with existing methods, the proposed method is more flexible as it does not require stringent parametric assumptions on the distributions of either the regression errors or the covariates. The finite sample performance of the proposed method is assessed through a simulation study and the analysis of a c-reactive protein dataset in the 2007--2008 National Health and Nutrition Examination Survey. This article has supplementary material online.

Suggested Citation

  • Huixia Judy Wang & Xingdong Feng, 2012. "Multiple Imputation for M -Regression With Censored Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 194-204, March.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:497:p:194-204
    DOI: 10.1080/01621459.2011.643198
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2011.643198
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2011.643198?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.

    Citations

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


    Cited by:

    1. Folefac D. Atem & Jing Qian & Jacqueline E. Maye & Keith A. Johnson & Rebecca A. Betensky, 2017. "Linear regression with a randomly censored covariate: application to an Alzheimer's study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 313-328, February.
    2. Yuanshan Wu & Guosheng Yin, 2017. "Multiple imputation for cure rate quantile regression with censored data," Biometrics, The International Biometric Society, vol. 73(1), pages 94-103, March.

    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:jnlasa:v:107:y:2012:i:497:p:194-204. 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/UASA20 .

    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.