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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’Informàtica i Matemàtica Aplicada

Altres contribucions: Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada
Autor: Filzmoser, Peter
Hron, Karel
Templ, Matthias
Data: 5 juny 2018
Resum: 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
Accés al document: http://hdl.handle.net/2072/319426
Llenguatge: eng
Editor: Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada
Drets: Tots els drets reservats
Matèria: Estadística matemàtica -- Congressos
Mathematical statistics -- Congresses
Anàlisi multivariable -- Congressos
Mathematical statistics -- Congresses
Títol: Robust Compositional Data Analysis
Tipus: info:eu-repo/semantics/conferenceObject
Repositori: Recercat

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