A bootstrap estimation scheme for chemical compositional data with nondetects

Journal of Chemometrics
By: , and 



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. 

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title A bootstrap estimation scheme for chemical compositional data with nondetects
Series title Journal of Chemometrics
DOI 10.1002/cem.2621
Volume 28
Issue 7
Year Published 2014
Language English
Publisher Wiley
Contributing office(s) Eastern Energy Resources Science Center
Description 15 p.
First page 585
Last page 599
Online Only (Y/N) N
Additional Online Files (Y/N) N
Google Analytic Metrics Metrics page