K-Clustering as a Detection Tool for Influential Subsets in Regression
作者:
J.Brian Gray,
RobertF. Ling,
期刊:
Technometrics
(Taylor Available online 1984)
卷期:
Volume 26,
issue 4
页码: 305-318
ISSN:0040-1706
年代: 1984
DOI:10.1080/00401706.1984.10487980
出版商: Taylor & Francis Group
关键词: Joint influence;Regression diagnostics;Cluster analysis;Hat matrix;Influential case;Leverage
数据来源: Taylor
摘要:
This article describes a new methodology for the detection of influential subsets in regression. The method is based on an adaptation of computational and graphical techniques used in cluster analysis and makes use of some general properties of influential subsets, but it is independent of any specific measure of influence. For small to moderate data sets the proposed method is computationally efficient, compared to existing search methods, and it identifies subset candidates that merit attention according to some or all measures of joint influence that have appeared in the literature to date. Examples are given illustrating the method applied to two data sets previously analyzed in published studies.
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