Variance estimation in censored quantile regression via induced smoothing

Lei Pang, Wenbin Lu and Huixia Judy Wang

Computational Statistics & Data Analysis, 2012, vol. 56, issue 4, 785-796

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.

Keywords: Censored quantile regression; Smoothing; Survival analysis; Variance estimation (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:4:p:785-796

DOI: 10.1016/j.csda.2010.10.018

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