Correlation analysis for proportions via updating and resampling

Compositional data arise frequently in practice, but their statistical analysis is not yet well developed. Compositions arise when nonnegative random vectors are mapped into the unit simplex via a closure operation, e.g., when the amounts of certain minerals in a soil sample are converted to percent...

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Main Authors: MONTI, GIANNA SERAFINA, Walther, G.
Other Authors: Monti, G, Walther, G
Format: Article in Journal/Newspaper
Language:English
Published: country:IN 2010
Subjects:
Online Access:http://hdl.handle.net/10281/22150
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spelling ftunivmilanobic:oai:boa.unimib.it:10281/22150 2023-11-12T04:20:26+01:00 Correlation analysis for proportions via updating and resampling MONTI, GIANNA SERAFINA Walther, G. Monti, G Walther, G 2010-03-30 http://hdl.handle.net/10281/22150 eng eng country:IN volume:3 issue:1 firstpage:17 lastpage:26 journal:JOURNAL OF STATISTICS: ADVANCES IN THEORY AND APPLICATIONS http://hdl.handle.net/10281/22150 compositional data bootstrap SECS-S/01 - STATISTICA info:eu-repo/semantics/article 2010 ftunivmilanobic 2023-10-17T22:18:23Z Compositional data arise frequently in practice, but their statistical analysis is not yet well developed. Compositions arise when nonnegative random vectors are mapped into the unit simplex via a closure operation, e.g., when the amounts of certain minerals in a soil sample are converted to percentages. Components in random compositions can never be stochastically independent, due to the spurious correlation introduced by taking the closure of the basis vectors. This paper aims to assess independence of the unobserved basis vectors based on the observed composition. We propose a resampling procedure that is based on an updating formula. A simulation study shows that this procedure works well in the case, where the components of the composition are roughly of the same size. We apply the procedure to a geochemical data set obtained from the Kola peninsula. Article in Journal/Newspaper kola peninsula Università degli Studi di Milano-Bicocca: BOA (Bicocca Open Archive) Kola Peninsula
institution Open Polar
collection Università degli Studi di Milano-Bicocca: BOA (Bicocca Open Archive)
op_collection_id ftunivmilanobic
language English
topic compositional data
bootstrap
SECS-S/01 - STATISTICA
spellingShingle compositional data
bootstrap
SECS-S/01 - STATISTICA
MONTI, GIANNA SERAFINA
Walther, G.
Correlation analysis for proportions via updating and resampling
topic_facet compositional data
bootstrap
SECS-S/01 - STATISTICA
description Compositional data arise frequently in practice, but their statistical analysis is not yet well developed. Compositions arise when nonnegative random vectors are mapped into the unit simplex via a closure operation, e.g., when the amounts of certain minerals in a soil sample are converted to percentages. Components in random compositions can never be stochastically independent, due to the spurious correlation introduced by taking the closure of the basis vectors. This paper aims to assess independence of the unobserved basis vectors based on the observed composition. We propose a resampling procedure that is based on an updating formula. A simulation study shows that this procedure works well in the case, where the components of the composition are roughly of the same size. We apply the procedure to a geochemical data set obtained from the Kola peninsula.
author2 Monti, G
Walther, G
format Article in Journal/Newspaper
author MONTI, GIANNA SERAFINA
Walther, G.
author_facet MONTI, GIANNA SERAFINA
Walther, G.
author_sort MONTI, GIANNA SERAFINA
title Correlation analysis for proportions via updating and resampling
title_short Correlation analysis for proportions via updating and resampling
title_full Correlation analysis for proportions via updating and resampling
title_fullStr Correlation analysis for proportions via updating and resampling
title_full_unstemmed Correlation analysis for proportions via updating and resampling
title_sort correlation analysis for proportions via updating and resampling
publisher country:IN
publishDate 2010
url http://hdl.handle.net/10281/22150
geographic Kola Peninsula
geographic_facet Kola Peninsula
genre kola peninsula
genre_facet kola peninsula
op_relation volume:3
issue:1
firstpage:17
lastpage:26
journal:JOURNAL OF STATISTICS: ADVANCES IN THEORY AND APPLICATIONS
http://hdl.handle.net/10281/22150
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