Bootstrapping Quantile Regression Estimators
证明了在确定性或随机回归元下，自助法分布弱收敛于分位数回归估计量的极限分布，因此自助百分位法构造的置信区间具有渐近正确的覆盖概率。
The asymptotic variance matrix of the quantile regression estimator depends on the density of the error. For both deterministic and random regressors, the bootstrap distribution is shown to converge weakly to the limit distribution of the quantile regression estimator in probability. Thus, the confidence intervals constructed by the bootstrap percentile method have asymptotically correct coverage probabilities.