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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

Other contributions: Universitat de Girona. Departament d’Informàtica i Matemàtica Aplicada
Author: Filzmoser, Peter
Hron, Karel
Templ, Matthias
Date: 2018 June 5
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/2072/319426
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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