Abstract

In agricultural experiments, if outliers are present in a data set, inference of experiment may be reversed. The purpose of this article is to develop a method for detection of subset of outlier vectors in block designs for incomplete multiresponse experiments in presence of masking. We defined an influence matrix comprising of Cook-statistics in its diagonal and product of two Cook-statistics in its off-diagonal positions. We then obtained eigenvectors corresponding to large eigenvalues of this matrix which can be used for identification of influential subsets. The proposed procedure has been illustrated with a real life data set.

中文翻译：

---

在存在掩蔽的情况下用于不完整多响应实验的块设计中识别多个有影响的观察的程序

摘要

在农业实验中，如果数据集中存在异常值，实验的推论可能会被推翻。本文的目的是开发一种方法，用于在存在掩蔽的情况下检测不完整多响应实验的块设计中的异常值向量子集。我们定义了一个影响矩阵，包括对角线上的 Cook 统计量和非对角线位置的两个 Cook 统计量的乘积。然后我们获得对应于该矩阵的大特征值的特征向量，可用于识别有影响的子集。拟议的程序已用现实生活中的数据集进行了说明。