Classical and robust regression analysis with compositional data

Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Mathematical Geosciences
Special issue
DOI of the original publication
Link
Series
Series number
Volume
53
Issue / Number
Pages / Duration
823-858
Patent number
Publisher / Publishing institution
Springer
Place of publication / Event location
London
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Compositional data carry their relevant information in the relationships (logratios) between the compositional parts. It is shown how this source of information can be used in regression modeling, where the composition could either form the response, or the explanatory part, or even both. An essential step to set up a regression model is the way how the composition(s) enter the model. Here, balance coordinates will be constructed that support an interpretation of the regression coefficients and allow for testing hypotheses of subcompositional independence. Both classical least-squares regression and robust MM regression are treated, and they are compared within different regression models at a real data set from a geochemical mapping project.
Keywords
Subject (DDC)
330 - Wirtschaft
510 - Mathematik
Project
Event
Exhibition start date
Exhibition end date
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Conference end date
Date of the last check
ISBN
ISSN
1874-8953
1874-8961
Language
English
Created during FHNW affiliation
No
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
'https://creativecommons.org/licenses/by/4.0/'
Citation
VAN DEN BOOGAART, K. G., Peter FILZMOSER, Karel HRON, Matthias TEMPL und Raimon TOLOSANA-DELGADO, 2021. Classical and robust regression analysis with compositional data. Mathematical Geosciences. 2021. Bd. 53, S. 823–858. DOI 10.1007/s11004-020-09895-w. Verfügbar unter: https://doi.org/10.26041/fhnw-11049