Non-detect Bootstrap Method for Estimating Distributional Parameters of Compositional Samples Revisited: a Multivariate Approach
dc.contributor.author
dc.date.accessioned
2017-02-17T08:54:29Z
dc.date.available
2017-02-17T08:54:29Z
dc.date.issued
2011-05-12
dc.identifier.isbn
978-84-87867-76-7
dc.identifier.uri
dc.description.abstract
Bootstrap resampling is an attractive, computationally-intensive approach for estimating population
parameters and their associated uncertainties. Values below detection limit- also referred
to as non-detects- frequently arise particularly when dealing with multivariate geochemical concentrations,
making the estimation of distributional parameters—mean, median, percentiles—a
difficult challenge. The bootstrap method can be used repeatedly for analyzing resampled versions
of the original data set. This way it is possible to estimate univariate distributional parameters
while also capturing the additional uncertainty due to missing information. Within this approach,
a method must be chosen to substitute non-detects with appropriate values given the compositional
nature of the data. This idea was first introduced by Olea (2008) in the previous CoDaWork’08
meeting. Making use of the isometric log-ratio transformation and analyzing one variable at a time,
he proposed a univariate bootstrap procedure where the distributional parameters of geochemical
components were modeled from bootstrap resamples considering different criteria to impute nondetects.
After conducting a sensitivity analysis on both proportion of non-detects and sample size,
the study concluded that when drawing randomly a value from the extrapolated tail below the detection
limit of the distribution best fitting the complete data—usually the log-normal distribution
for geochemical data—the bootstrap estimates turned out to be more accurate than those obtained
using simple imputation methods. Rather than analyzing each variable separately, here we make
a step further to get the most of the covariance structure of the data set, extending the univariate
approach for replacing non-detects to a multivariate setting. As a test bench, a number of data
sets containing non-detects are artificially generated from real geochemical data and used to evaluate
the performance of different replacement methods within the bootstrap process. First results
show improved results when non-detects are replaced by random values drawn from a conditional
truncated additive logistic model
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Universitat Politècnica de Catalunya. Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE)
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Reproducció digital del document publicat a:
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© International Workshop on Compositional Data Analysis (4th: 2011: Sant Feliu de GuÍxols, Girona). CODAWORK 2011: International Workshop on Compositional Data Analysis, hold on May 9-13rd. 2011, Sant Feliu de Guíxols, Girona
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Llibres / Capítols de LLibre (D-IMAE)
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Tots els drets reservats
dc.subject
dc.title
Non-detect Bootstrap Method for Estimating Distributional Parameters of Compositional Samples Revisited: a Multivariate Approach
dc.type
info:eu-repo/semantics/bookPart
dc.rights.accessRights
info:eu-repo/semantics/openAccess