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Smoothed Jackknife Empirical Likelihood for Weighted Rank Regression with Censored Data

Author

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  • Longlong Huang

    (Department of Mathematics and Statistics, University of Calgary, Canada)

  • Karen Kopciuk

    (Department of Mathematics and Statistics, University of Calgary, Canada)

  • Xuewen Lu

    (Department of Mathematics and Statistics, University of Calgary, Canada)

Abstract

To make inference for the semiparametric accelerated failure time (AFT) model with right censored data, which may contain outlying response or covariate values, we propose a smoothed jackknife empirical likelihood (JEL) method for the U -statistic obtained from a weighted smoothed rank estimating function. The jackknife empirical likelihood ratio is shown to be a standard chi-squared statistic. The new method improves upon the inference of the normal approximation method and possesses desirable important properties of easy computation and double robustness against influence of both outlying response and covariates. The advantages of the new method are demonstrated by simulation studies and data analyses.

Suggested Citation

  • Longlong Huang & Karen Kopciuk & Xuewen Lu, 2018. "Smoothed Jackknife Empirical Likelihood for Weighted Rank Regression with Censored Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(2), pages 48-67, April.
    • Handle: RePEc:adp:jbboaj:v:6:y:2018:i:2:p:48-67
    DOI: 10.19080/BBOAJ.2018.06.555685
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    References listed on IDEAS

    as
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