摘要翻译：
我们建议平滑整个目标函数，而不仅仅是检查函数，在线性分位数回归上下文。所得的平滑分位数回归估计不仅比标准估计得到更低的均方误差和更精确的Bahadur-Kiefer表示，而且是渐近可微的。我们利用后者提出了一个不受维数诅咒的分位数密度估计器。这意味着估计条件密度函数而不用担心协变量向量的维数。它还允许两阶段有效的分位数回归估计。对于带宽和分位数，我们的渐近理论是一致的。最后，我们提出了一个经验法则来选择平滑带宽，应该很好地接近最佳带宽。仿真结果表明，我们的平滑分位数回归估计器在有限样本中确实表现得很好。
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英文标题：
《Smoothing quantile regressions》
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作者：
Marcelo Fernandes, Emmanuel Guerre, Eduardo Horta
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最新提交年份：
2019
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分类信息：

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：
  We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer representation than the standard estimator, but it is also asymptotically differentiable. We exploit the latter to propose a quantile density estimator that does not suffer from the curse of dimensionality. This means estimating the conditional density function without worrying about the dimension of the covariate vector. It also allows for two-stage efficient quantile regression estimation. Our asymptotic theory holds uniformly with respect to the bandwidth and quantile level. Finally, we propose a rule of thumb for choosing the smoothing bandwidth that should approximate well the optimal bandwidth. Simulations confirm that our smoothed quantile regression estimator indeed performs very well in finite samples.
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PDF链接：
https://arxiv.org/pdf/1905.08535