A bootstrap estimation scheme for chemical compositional data with nondetects
dc.contributor.author
dc.date.accessioned
2015-09-16T12:30:02Z
dc.date.available
2015-09-16T12:30:02Z
dc.date.issued
2014-07
dc.identifier.issn
0886-9383
dc.identifier.uri
dc.description.abstract
The bootstrap method is commonly used to estimate the distribution of estimators and their associated uncertainty when explicit analytic expressions are not available or are difficult to obtain. It has been widely applied in environmental and geochemical studies, where the data generated often represent parts of whole, typically chemical concentrations. This kind of constrained data is generically called compositional data, and they require specialised statistical methods to properly account for their particular covariance structure. On the other hand, it is not unusual in practice that those data contain labels denoting nondetects, that is, concentrations falling below detection limits. Nondetects impede the implementation of the bootstrap and represent an additional source of uncertainty that must be taken into account. In this work, a bootstrap scheme is devised that handles nondetects by adding an imputation step within the resampling process and conveniently propagates their associated uncertainly. In doing so, it considers the constrained relationships between chemical concentrations originated from their compositional nature. Bootstrap estimates using a range of imputation methods, including new stochastic proposals, are compared across scenarios of increasing difficulty. They are formulated to meet compositional principles following the log-ratio approach, and an adjustment is introduced in the multivariate case to deal with nonclosed samples. Results suggest that nondetect bootstrap based on model-based imputation is generally preferable. A robust approach based on isometric log-ratio transformations appears to be particularly suited in this context. Computer routines in the R statistical programming language are provided
dc.description.sponsorship
This research has been supported by the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS), the Spanish Ministry of Economy and Competitiveness under the project 'METRICS' Ref. MTM2012-33236 and the Agencia de Gestio d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya under the project Ref. 2009SGR424
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Wiley
dc.relation
info:eu-repo/grantAgreement/MINECO//MTM2012-33236/ES/METODOS ESTADISTICOS EN ESPACIOS RESTRINGIDOS/
AGAUR/2009-2014/2009 SGR-424
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Reproducció digital del document publicat a: http://dx.doi.org/10.1002/cem.2621
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© Journal of Chemometrics, 2014, vol. 28, núm. 7, p. 585-599
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Articles publicats (D-IMA)
dc.rights
Tots els drets reservats
dc.subject
dc.title
A bootstrap estimation scheme for chemical compositional data with nondetects
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/embargoedAccess
dc.embargo.terms
Cap
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2026-01-01
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
022346
dc.contributor.funder
dc.relation.ProjectAcronym
dc.identifier.eissn
1099-128X