Classical and robust regression analysis with compositional data

van den Boogaart, K.G.; Filzmoser, P.; Hron, K.; Templ, M.; Tolosano-Delgado, R.

doi:10.1007/s11004-020-09895-w

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22472

Full metadata record

DC Field	Value	Language
dc.contributor.author	van den Boogaart, K.G.	-
dc.contributor.author	Filzmoser, P.	-
dc.contributor.author	Hron, K.	-
dc.contributor.author	Templ, M.	-
dc.contributor.author	Tolosano-Delgado, R.	-
dc.date.accessioned	2021-05-12T12:11:54Z	-
dc.date.available	2021-05-12T12:11:54Z	-
dc.date.issued	2020-10-06	-
dc.identifier.issn	1874-8961	de_CH
dc.identifier.issn	1874-8953	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/22472	-
dc.description.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.	de_CH
dc.language.iso	en	de_CH
dc.publisher	Springer	de_CH
dc.relation.ispartof	Mathematical Geosciences	de_CH
dc.rights	http://creativecommons.org/licenses/by/4.0/	de_CH
dc.subject	Balances	de_CH
dc.subject	Robust regression	de_CH
dc.subject	GEMAS project	de_CH
dc.subject	Hypothesis testing	de_CH
dc.subject	Robust bootstrap	de_CH
dc.subject.ddc	510: Mathematik	de_CH
dc.title	Classical and robust regression analysis with compositional data	de_CH
dc.type	Beitrag in wissenschaftlicher Zeitschrift	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Institut für Datenanalyse und Prozessdesign (IDP)	de_CH
dc.identifier.doi	10.1007/s11004-020-09895-w	de_CH
dc.identifier.doi	10.21256/zhaw-22472	-
zhaw.funding.eu	No	de_CH
zhaw.issue	5	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.end	858	de_CH
zhaw.pages.start	823	de_CH
zhaw.publication.status	publishedVersion	de_CH
zhaw.volume	53	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.webfeed	Statistik und Quantitative Finance	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	No	de_CH
Appears in collections:	Publikationen School of Engineering

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van den Boogaart, K. G., Filzmoser, P., Hron, K., Templ, M., & Tolosano-Delgado, R. (2020). Classical and robust regression analysis with compositional data. Mathematical Geosciences, 53(5), 823–858. https://doi.org/10.1007/s11004-020-09895-w

van den Boogaart, K.G. et al. (2020) ‘Classical and robust regression analysis with compositional data’, Mathematical Geosciences, 53(5), pp. 823–858. Available at: https://doi.org/10.1007/s11004-020-09895-w.

K. G. van den Boogaart, P. Filzmoser, K. Hron, M. Templ, and R. Tolosano-Delgado, “Classical and robust regression analysis with compositional data,” Mathematical Geosciences, vol. 53, no. 5, pp. 823–858, Oct. 2020, doi: 10.1007/s11004-020-09895-w.

VAN DEN BOOGAART, K.G., P. FILZMOSER, K. HRON, M. TEMPL und R. TOLOSANO-DELGADO, 2020. Classical and robust regression analysis with compositional data. Mathematical Geosciences. 6 Oktober 2020. Bd. 53, Nr. 5, S. 823–858. DOI 10.1007/s11004-020-09895-w

van den Boogaart, K.G., P. Filzmoser, K. Hron, M. Templ, and R. Tolosano-Delgado. 2020. “Classical and Robust Regression Analysis with Compositional Data.” Mathematical Geosciences 53 (5): 823–58. https://doi.org/10.1007/s11004-020-09895-w.

van den Boogaart, K. G., et al. “Classical and Robust Regression Analysis with Compositional Data.” Mathematical Geosciences, vol. 53, no. 5, Oct. 2020, pp. 823–58, https://doi.org/10.1007/s11004-020-09895-w.