Fast Very Robust Methods for the Detection of Multiple Outliers
通过从随机起点重复简单的前向搜索，获得足够稳健的参数估计以揭示被掩盖的多个异常值，并用钟乳石图展示模式稳定性。
A few repeats of a simple forward search from a random starting point are shown to provide sufficiently robust parameter estimates to reveal masked multiple outliers. The stability of the patterns obtained is exhibited by the stalactite plot. The robust estimators used are least median of squares for regression and the minimum volume ellipsoid for multivariate outliers. The forward search also has potential as an algorithm for calculation of these parameter estimates. For large problems, parallel computing provides appreciable reduction in computational time.