Fast calibrations of the forward search for testing multiple outliers in regression
Fast calibrations of the forward search for testing multiple outliers in regression
复制标题
快速校准前向搜索以测试回归中的多个异常值
DOI：
10.1007/s11634-007-0007-y
复制
发表时间：
2007
影响因子：
1.6
通讯作者：
A. Atkinson
中科院分区：
文献类型：
--
作者：
M. Riani;A. Atkinson
本文考虑了回归模型中多个异常值的检验问题，并提供了作为检验统计量的最小删除残差的零分布的快速逼近。由于直接模拟观测数和参数数的每种组合太耗时，因此描述了使用简单正态样本近似检验统计量的点向分布的方法。一种近似是基于对简单模拟结果的调整。另一种方法使用来自折叠分布的顺序统计属性移动到仿真可用的显著性水平之外。对带有beta误差的数据和关于生存时间的转换数据的分析表明，包含我们的界限的图形方法是有用的。
The paper considers the problem of testing for multiple outliers in a regression model and provides fast approximations to the null distribution of the minimum deletion residual used as a test statistic. Since direct simulation of each combination of number of observations and number of parameters is too time consuming, methods using simple normal samples are described for approximating the pointwise distribution of the test statistic. One approximation is based on adjustments to the results of simple simulations. The other uses properties of order statistics from foldedtdistributions to move outside the significance levels available by simulation. Analyses of data with beta errors and of transformed data on survival times demonstrate the usefulness in graphical methods of the inclusion of our bounds.