Interpretation of multivariate outliers for compositional data

被引：92
|
作者：
Filzmoser, Peter [1 ]
Hron, Karel [2 ]
Reimann, Clemens [3 ]
机构：
[1] Vienna Univ Technol, Dept Stat & Probabil Theory, A-1040 Vienna, Austria
[2] Palacky Univ, Fac Sci, Dept Math Anal & Applicat Math, CZ-77146 Olomouc, Czech Republic
[3] Geol Survey Norway NGU, N-7491 Trondheim, Norway
来源：
COMPUTERS & GEOSCIENCES | 2012年 / 39卷
关键词：
Compositional data; Log-ratio transformations; Outlier detection; Compositional biplot; ROBUST METHODS; GEOCHEMISTRY;
D O I：
10.1016/j.cageo.2011.06.014
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Compositional data-and most data in geochemistry are of this type-carry relative rather than absolute information. For multivariate outlier detection methods this implies that not the given data but appropriately transformed data need to be used. We use the isometric logratio (ilr) transformation, which seems to be generally the most proper one for theoretical and practical reasons. In this space it is difficult to interpret the outliers, because the reason for outlyingness can be complex. Therefore we introduce tools that support the interpretation of outliers by representing multivariate information in biplots, maps, and univariate scatterplots. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码：77 / 85
页数：9
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