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Variance estimation in censored quantile regression via induced smoothing

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  • Pang, Lei
  • Lu, Wenbin
  • Wang, Huixia Judy

Abstract

Statistical inference in censored quantile regression is challenging, partly due to the unsmoothness of the quantile score function. A new procedure is developed to estimate the variance of the Bang and Tsiatis inverse-censoring-probability weighted estimator for censored quantile regression by employing the idea of induced smoothing. The proposed variance estimator is shown to be asymptotically consistent. In addition, a numerical study suggests that the proposed procedure performs well in finite samples, and it is computationally more efficient than the commonly used bootstrap method.

Suggested Citation

  • Pang, Lei & Lu, Wenbin & Wang, Huixia Judy, 2012. "Variance estimation in censored quantile regression via induced smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 785-796.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:4:p:785-796
    DOI: 10.1016/j.csda.2010.10.018
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    References listed on IDEAS

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    Cited by:

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    3. Li, Haifen & Zhang, Jiajia & Tang, Yincai, 2012. "Induced smoothing for the semiparametric accelerated hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4312-4319.
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    7. Qiu, Zhiping & Peng, Limin & Manatunga, Amita & Guo, Ying, 2019. "A smooth nonparametric approach to determining cut-points of a continuous scale," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 186-210.
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    10. Zexi Cai & Tony Sit, 2023. "On interquantile smoothness of censored quantile regression with induced smoothing," Biometrics, The International Biometric Society, vol. 79(4), pages 3549-3563, December.

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