论文标题
偏置校正和对分位数函数的均匀推断
Bias correction and uniform inference for the quantile density function
论文作者
论文摘要
对于分位数密度函数的内核估计器（分位数函数的导数），我显示了如何执行边界偏置校正，确定偏置校正估计器的强均匀一致性的速率，并构造在整个域$ [0,1] $上均不均匀地均匀地均匀地均匀的置信带。所提出的程序依赖于该偏差校正估计量的关键性和高斯近似值的已知抗浓缩特性。
For the kernel estimator of the quantile density function (the derivative of the quantile function), I show how to perform the boundary bias correction, establish the rate of strong uniform consistency of the bias-corrected estimator, and construct the confidence bands that are asymptotically exact uniformly over the entire domain $[0,1]$. The proposed procedures rely on the pivotality of the studentized bias-corrected estimator and known anti-concentration properties of the Gaussian approximation for its supremum.
