摘要翻译：
非参数核密度估计和局部多项式回归估计在统计学、经济学和许多其他学科中非常流行。它们通常在应用工作中使用，要么作为主要经验分析的一部分，要么作为进入其他估计或推断程序的初步成分。本文介绍了nprobust软件包的主要方法和数值特性，该软件包提供了一系列非参数核密度和局部多项式回归方法的估计和推断过程，在R和Stata统计平台上实现。该包不仅包括经典的带宽选择、估计和推断方法（Wand and Jones，1995；Fan and Gijbels，1996)，还包括统计学和计量经济学文献中的其他最新发展，如稳健的偏差校正推断和覆盖误差最优带宽选择(Calonico，Cattaneo和Farrell，2018，2019)。此外，本文还提出了一种在实际中估计最优带宽的简单方法，该方法无论在具体的评估点上，即无论在边界点还是在内点上实现，都能获得最优的均方误差收敛速度。通过一个经验应用和模拟数据说明了数值性能，并与其他R包进行了详细的数值比较。
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英文标题：
《nprobust: Nonparametric Kernel-Based Estimation and Robust
  Bias-Corrected Inference》
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作者：
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
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最新提交年份：
2019
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分类信息：

一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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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：
  Nonparametric kernel density and local polynomial regression estimators are very popular in Statistics, Economics, and many other disciplines. They are routinely employed in applied work, either as part of the main empirical analysis or as a preliminary ingredient entering some other estimation or inference procedure. This article describes the main methodological and numerical features of the software package nprobust, which offers an array of estimation and inference procedures for nonparametric kernel-based density and local polynomial regression methods, implemented in both the R and Stata statistical platforms. The package includes not only classical bandwidth selection, estimation, and inference methods (Wand and Jones, 1995; Fan and Gijbels, 1996), but also other recent developments in the statistics and econometrics literatures such as robust bias-corrected inference and coverage error optimal bandwidth selection (Calonico, Cattaneo and Farrell, 2018, 2019). Furthermore, this article also proposes a simple way of estimating optimal bandwidths in practice that always delivers the optimal mean square error convergence rate regardless of the specific evaluation point, that is, no matter whether it is implemented at a boundary or interior point. Numerical performance is illustrated using an empirical application and simulated data, where a detailed numerical comparison with other R packages is given.
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PDF链接：
https://arxiv.org/pdf/1906.00198