Regression Diagnostics for Rank-Based Methods
研究了线性模型秩拟合的残差性质，提出了检测异常值和影响点的诊断技术，这些技术能发现模型未捕捉的曲率，并在多种误差分布下保持高效。
Abstract Residual plots and diagnostic techniques have become important tools in examining the least squares fit of a linear model. In this article we explore the properties of the residuals from a rank-based fit of the model. We present diagnostic techniques that detect outlying cases and cases that have an influential effect on the rank-based fit. We show that the residuals from this fit can be used to detect curvature not accounted for by the fitted model. Furthermore, our diagnostic techniques inherit the excellent efficiency properties of the rank-based fit over a wide class of error distributions, including asymmetric distributions. We illustrate these techniques with several examples.