AI 中文总结
研究提出基于局部多项式估计分位数密度函数及导数的新方法，该估计器在边界处性质更优，证明其渐近正态性并与其他估计器比较偏差、方差及边界性质。
AI 中文摘要
本文提出一种基于局部多项式估计的分位数密度函数（稀疏函数）及其导数的非参数估计新方法。该估计器在边界处比经典分位数密度估计器具有更优性质。文中证明了渐近正态性，并将偏差、渐近方差以及边界性质与其他估计器进行比较。
英文摘要
A new approach for nonparametric estimation of the quantile density function (sparsity function) and its derivatives is suggested which is based on local polynomial estimation. The estimator has more advantageous properties at the boundaries than classical quantile density estimators. Asymptotic normality is shown and the bias, asymptotic variance as well as boundary properties are compared with other estimators.
