An Evaluation of Bootstrap Methods for Outlier Detection in Least Squares Regression

MICHAEL A. MARTIN; STEVEN ROBERTS

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An Evaluation of Bootstrap Methods for Outlier Detection in Least Squares Regression

机译：最小二乘回归中离群值检测的Bootstrap方法评估

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Outlier detection is a critical part of data analysis, and the use of Studentized residuals from regression models fit using least squares is a very common approach to identifying discordant observations in linear regression problems. In this paper we propose a bootstrap approach to constructing critical points for use in outlier detection in the context of least-squares Studentized residuals, and find that this approach allows naturally for mild departures in model assumptions such as non-Normal error distributions. We illustrate our methodology through both a real data example and simulated data.

机译：离群值检测是数据分析的关键部分，使用最小二乘法拟合回归模型中的学生化残差是识别线性回归问题中不一致观测值的一种非常常用的方法。在本文中，我们提出了一种Bootstrap方法来构造临界点，以在最小二乘学生化残差的背景下进行离群值检测，并发现该方法自然允许模型假设（例如非正态误差分布）中的轻微偏离。我们通过真实数据示例和模拟数据来说明我们的方法。

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来源
《Journal of applied statistics》 |2006年第7期|p.703-720|共18页
作者
MICHAEL A. MARTIN; STEVEN ROBERTS;
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作者单位

School of Finance and Applied Statistics, Australian National University, Canberra, ACT 0200, Australia;

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正文语种 eng
中图分类统计学;
关键词
case-based resampling; error distribution; externally studentized residuals; internally studentized residuals; jackknife-after-bootstrap; residual-based resampling; RSTUDENT;

机译：基于案例的重采样;错误分布;外部学习的残差;内部学习的残差;自举后的折刀;基于残差的重采样;RSTUDENT;

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An Evaluation of Bootstrap Methods for Outlier Detection in Least Squares Regression

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