Locally Weighted Censored Quantile Regression

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Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood (2016, methodology): “…(3.2) Various estimation methods have been developed for censored quantile regression, including Portnoy (2003), Ying et al. (1995), Peng & Huang (2008) and Wang & Wang (2009) for random censoring, Lin et al. (2012) for double censoring and Powell (1986) and Tang et al. (2012) for fixed censoring.”
A quantile regression estimator for censored data (2013, methodology): “We take the ﬁrst example from Wang and Wang [22] to generate failure time from the following i.i.d. error model ∼ standard normal random variables.”
Handbook of survival analysis (2014, background): “Some efforts, for example, by Wang and Wang (2009), have been made to address a local linear quantile regression model (i.”
Conditional quantile screening in ultrahigh-dimensional heterogeneous data (2015, unknown): “…a weight-adjusted version of dk(t), rk(t) = E { [τ − ω(F)I {X < Qτ (Y )}] I (Zk < t) } , where F(y) = pr(Y y) and the weight function ω(F) = ⎧⎨ ⎩ 1, = 1 or F(C) > τ, τ − F(C) 1 − F(C) , = 0, F(C) τ redistributes the masses of censored observations to the right (Portnoy, 2003; Wang & Wang, 2009).”