摘要翻译：
在本文中，作者提出了一种基于加权样本的加权核密度估计器(wKDE)来估计目标种群的密度。讨论了wKDE的带宽选择问题。介绍了三种基于平均积分平方误差的带宽估计器，并通过蒙特卡罗仿真说明了它们的性能。研究了最小二乘交叉验证方法和自适应权核密度估计器。本文还考虑了区间有界数据的边界问题，并将新方法应用于信息截尾的实际数据集。
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英文标题：
《Bandwidth Selection for Weighted Kernel Density Estimation》
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作者：
Bin Wang, Xiaofeng Wang
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最新提交年份：
2011
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  In the this paper, the authors propose to estimate the density of a targeted population with a weighted kernel density estimator (wKDE) based on a weighted sample. Bandwidth selection for wKDE is discussed. Three mean integrated squared error based bandwidth estimators are introduced and their performance is illustrated via Monte Carlo simulation. The least-squares cross-validation method and the adaptive weight kernel density estimator are also studied. The authors also consider the boundary problem for interval bounded data and apply the new method to a real data set subject to informative censoring.
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PDF链接：
https://arxiv.org/pdf/709.1616