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Nonparametric smoothed quantile difference estimation for length-biased and right-censored data

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  • Jianhua Shi
  • Yutao Liu
  • Jinfeng Xu

Abstract

We consider the nonparametric analysis of length-biased and right-censored data (LBRC) by quantile difference. With its desirable properties such as superior robustness and easy interpretation, quantile difference has been widely used in practice, in particular, for missing and survival data. Existing approaches for nonparametric estimation of quantile difference in length-biased survival data, however, exhibit some drawbacks such as non-smoothness and instabilities. To overcome these difficulties, we proposed a smoothed quantile difference estimation approach to improve its estimating efficiency with its validity justified by asymptotic theories. Simulations are also conducted to evaluate the performance of the proposed estimator. An application to the Channing house data is further provided for illustration.

Suggested Citation

  • Jianhua Shi & Yutao Liu & Jinfeng Xu, 2022. "Nonparametric smoothed quantile difference estimation for length-biased and right-censored data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(10), pages 3237-3252, May.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:10:p:3237-3252
    DOI: 10.1080/03610926.2020.1791340
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