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Title: Detection of influential points by convex hull volume minimization (English)
Author: Tichavský, Petr
Author: Boček, Pavel
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 34
Issue: 5
Year: 1998
Pages: [515]-534
Summary lang: English
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Category: math
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Summary: A method of geometrical characterization of multidimensional data sets, including construction of the convex hull of the data and calculation of the volume of the convex hull, is described. This technique, together with the concept of minimum convex hull volume, can be used for detection of influential points or outliers in multiple linear regression. An approximation to the true concept is achieved by ordering the data into a linear sequence such that the volume of the convex hull of the first $n$ terms in the sequence grows as slowly as possible with $n$. The performance of the method is demonstrated on four well known data sets. The average computational complexity needed for the ordering is estimated by $O(N^{2+(p-1)/(p+1)})$ for large $N$, where $N$ is the number of observations and $p$ is the data dimension, i. e. the number of predictors plus 1. (English)
Keyword: multiple linear regression
Keyword: detection of influential points
MSC: 62H10
MSC: 62J05
MSC: 90C59
MSC: 94A13
idZBL: Zbl 1274.94019
idMR: MR1663724
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Date available: 2009-09-24T19:20:06Z
Last updated: 2015-03-28
Stable URL: http://hdl.handle.net/10338.dmlcz/135240
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Reference: [19] Rousseeuw P. J.: Least median of squares regressio.
Reference: [22] Rousseeuw P. J., Zomeren B. C. van: Unmasking multivariate outliers and leverage points (with comments).J. Amer. Statist. Assoc
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