Bootstrap confidence intervals for trilinear partial least squares regression

The boostrap is a successful technique to obtain confidence limits for estimates where it is theoretically impossible to establish an exact expression thereunto. Trilinear partial least squares regression (tri-PLS) is an estimator for which this is the case; in the current paper we thus propose to a...

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Bibliographic Details
Published inAnalytica chimica acta Vol. 544; no. 1; pp. 153 - 158
Main Authors Serneels, Sven, Van Espen, Pierre J.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Amsterdam Elsevier B.V 15.07.2005
Elsevier
Subjects
Analytical chemistry
Bootstrap
Chemistry
Confidence interval
Exact sciences and technology
Prediction
Tri-PLS
Trilinear partial least squares regression
Uncertainty
Trilinear partial least squares regression
Bootstrap
Uncertainty
Tri-PLS
Prediction
Confidence interval
PLS regression
Simulation
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Summary:The boostrap is a successful technique to obtain confidence limits for estimates where it is theoretically impossible to establish an exact expression thereunto. Trilinear partial least squares regression (tri-PLS) is an estimator for which this is the case; in the current paper we thus propose to apply the bootstrap in order to obtain confidence intervals for the predictions made by tri-PLS. By dint of an extensive simulation study, we show that bootstrap confidence intervals have a desirable coverage. Finally, we apply the method to an identification problem of micro-organisms and show that from the bootstrap confidence intervals, the organisms can (up to a misclassification probability of 3.5%) correctly be identified.
Bibliography:SourceType-Scholarly Journals-2
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ObjectType-Conference Paper-1
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SourceType-Conference Papers & Proceedings-1
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ISSN:0003-2670
1873-4324
DOI:10.1016/j.aca.2005.02.012