Subsampling Method for Robust Estimation of Regression Models

• 作者: Min Tsao Xiao Ling
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: SUBSAMPLING ALGORITHM ROBUST Regression OUTLIERS BOOTSTRAP GOODNESS-OF-FIT
• 年，卷（期）: tjxqkyw_2012,（3）
• 页码范围: 281-296
• 页数: 16页
• 分类号: R73

摘要

We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method to find a good sample from regression data contaminated with outliers, and then applies the classical method to the good sample to produce robust estimates of the regression model parameters. The subsampling method is a computational method rooted in the bootstrap methodology which trades analytical treatment for intensive computation;it finds the good sample through repeated fitting of the regression model to many random subsamples of the contaminated data instead of through an analytical treatment of the outliers. The subsampling method can be applied to all regression models for which non-robust classical methods are available. In the present paper, we focus on the basic formulation and robustness property of the subsampling method that are valid for all regression models. We also discuss variations of the method and apply it to three examples involving three different regression models.