Smoothed quantile regression for censored residual life

Kyu Hyun Kim (), Daniel J. Caplan () and Sangwook Kang ()
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Kyu Hyun Kim: Yonsei University
Daniel J. Caplan: University of Iowa
Sangwook Kang: Yonsei University

Computational Statistics, 2023, vol. 38, issue 2, No 18, 1022 pages

Abstract: Abstract We consider a regression modeling of the quantiles of residual life, remaining lifetime at a specific time. We propose a smoothed induced version of the existing non-smooth estimating equations approaches for estimating regression parameters. The proposed estimating equations are smooth in regression parameters, so solutions can be readily obtained via standard numerical algorithms. Moreover, the smoothness in the proposed estimating equations enables one to obtain a robust sandwich-type covariance estimator of regression estimators aided by an efficient resampling method. To handle data subject to right censoring, the inverse probability of censoring distribution is used as a weight. The consistency and asymptotic normality of the proposed estimator are established. Extensive simulation studies are conducted to validate the proposed estimator’s performance in various finite samples settings. We apply the proposed method to dental study data evaluating the longevity of dental restorations.

Keywords: Induced smoothing; Inverse of censoring weighting; Median regression; Resampling; Sandwich estimator; Survival analysis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-022-01262-z

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