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dc.contributor.authorBouckaert, Remco R.
dc.contributor.authorFrank, Eibe
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorFletcher, Dale
dc.date.accessioned2011-11-11T03:03:51Z
dc.date.available2011-11-11T03:03:51Z
dc.date.issued2011
dc.identifier.citationBouckaert, R.R., Frank, E., Holmes, G. & Fletcher, D. (2011). A comparison of methods for estimating prediction intervals in NIR spectroscopy: Size matters. Chemometrics and Intelligent Laboratory Systems, 109(2), 139-145.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/5877
dc.description.abstractIn this article we demonstrate that, when evaluating a method for determining prediction intervals, interval size matters more than coverage because the latter can be fixed at a chosen confidence level with good reliability. To achieve the desired coverage, we employ a simple non-parametric method to compute prediction intervals by calibrating estimated prediction errors, and we extend the basic method with a continuum correction to deal with small data sets. In our experiments on a collection of several NIR data sets, we evaluate several existing methods of computing prediction intervals for partial least-squares (PLS) regression. Our results show that, when coverage is fixed at a chosen confidence level, and the number of PLS components is selected to minimize squared error of point estimates, interval estimation based on the classic ordinary least-squares method produces the narrowest intervals, outperforming the U-deviation method and linearization, regardless of the confidence level that is chosen.en_NZ
dc.language.isoen
dc.publisherElsevieren_NZ
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S0169743911001742en_NZ
dc.subjectNIRen_NZ
dc.subjectprediction intervalen_NZ
dc.subjectPLS regressionen_NZ
dc.subjectexperimental designen_NZ
dc.subjectMachine learning
dc.titleA comparison of methods for estimating prediction intervals in NIR spectroscopy: Size mattersen_NZ
dc.typeJournal Articleen_NZ
dc.identifier.doi10.1016/j.chemolab.2011.08.008en_NZ
dc.relation.isPartOfChemometrics and Intelligent Laboratory Systemsen_NZ
pubs.begin-page139en_NZ
pubs.editionDecemberen_NZ
pubs.elements-id36672
pubs.end-page145en_NZ
pubs.issue2en_NZ
pubs.volume109en_NZ


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