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Robust Compositional Data Analysis

Many practical data sets contain outliers or other forms of data inhomogeneities. Robuststatistics offers concepts how to deal with these situations where the data do not follow strictmodel assumptions. These concepts are designed for the usual Euclidean space, and they can beeasily applied to compositional data if they are represented in this space as well. It turns outthat the isometric logratio (ilr) transformation is best suitable in the context of robust estimation.Depending on the method applied, an interpretation of result is usually done in a back-transformedspace

Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada

Other contributions: Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada
Author: Filzmoser, Peter
Hron, Karel
Templ, Matthias
Abstract: Many practical data sets contain outliers or other forms of data inhomogeneities. Robuststatistics offers concepts how to deal with these situations where the data do not follow strictmodel assumptions. These concepts are designed for the usual Euclidean space, and they can beeasily applied to compositional data if they are represented in this space as well. It turns outthat the isometric logratio (ilr) transformation is best suitable in the context of robust estimation.Depending on the method applied, an interpretation of result is usually done in a back-transformedspace
Document access: http://hdl.handle.net/2072/273633
Language: eng
Publisher: Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada
Rights: Tots els drets reservats
Subject: Estadística matemàtica -- Congressos
Mathematical statistics -- Congresses
Anàlisi multivariable -- Congressos
Mathematical statistics -- Congresses
Title: Robust Compositional Data Analysis
Type: info:eu-repo/semantics/conferenceObject
Repository: Recercat

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