摘要翻译：
在未知误差分布为非参数的线性回归装置下，我们考虑了以观察到的辅助统计量为条件的回归系数的推理过程。在正则性条件下建立了回归系数估计的条件渐近正态性，并在形式上证明了在条件推理过程中插入核型密度估计的方法。仿真结果表明，该方法在构造置信区间时能得到准确的条件覆盖概率。插件方法可以与配置多抽样结合应用，以导出自适应于对比性场景的对抗的鲁棒条件估计器。我们通过研究位置估计器在各种对抗下的条件均方误差来证明这一点，并成功地将配置多采样扩展到非参数环境。
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英文标题：
《Nonparametric Conditional Inference for Regression Coefficients with
  Application to Configural Polysampling》
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作者：
Yvonne Ho and Stephen Lee
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最新提交年份：
2007
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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：
  We consider inference procedures, conditional on an observed ancillary statistic, for regression coefficients under a linear regression setup where the unknown error distribution is specified nonparametrically. We establish conditional asymptotic normality of the regression coefficient estimators under regularity conditions, and formally justify the approach of plugging in kernel-type density estimators in conditional inference procedures. Simulation results show that the approach yields accurate conditional coverage probabilities when used for constructing confidence intervals. The plug-in approach can be applied in conjunction with configural polysampling to derive robust conditional estimators adaptive to a confrontation of contrasting scenarios. We demonstrate this by investigating the conditional mean squared error of location estimators under various confrontations in a simulation study, which successfully extends configural polysampling to a nonparametric context.
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PDF链接：
https://arxiv.org/pdf/710.5675