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Robust Compositional Data Analysis

Many practical data sets contain outliers or other forms of data inhomogeneities. Robust statistics offers concepts how to deal with these situations where the data do not follow strict model assumptions. These concepts are designed for the usual Euclidean space, and they can be easily applied to compositional data if they are represented in this space as well. It turns out that 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-transformed space

Universitat de Girona. Departament d鈥橧nform脿tica i Matem脿tica Aplicada

Other contributions: Universitat de Girona. Departament d鈥橧nform脿tica i Matem脿tica Aplicada
Author: Filzmoser, Peter
Hron, Karel
Templ, Matthias
Abstract: Many practical data sets contain outliers or other forms of data inhomogeneities. Robust statistics offers concepts how to deal with these situations where the data do not follow strict model assumptions. These concepts are designed for the usual Euclidean space, and they can be easily applied to compositional data if they are represented in this space as well. It turns out that 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-transformed space
Document access: http://hdl.handle.net/2072/299065
Language: eng
Publisher: Universitat de Girona. Departament d鈥橧nform脿tica i Matem脿tica Aplicada
Rights: Tots els drets reservats
Subject: Estad铆stica matem脿tica -- Congressos
Mathematical statistics -- Congresses
An脿lisi multivariable -- Congressos
Mathematical statistics -- Congresses
Title: Robust Compositional Data Analysis
Type: info:eu-repo/semantics/conferenceObject
Repository: Recercat

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