Application of Two Novel Partial Least Squares-Based Regression Methods to the Analysis of Spectral Datasets

dc.contributor.author Fuhrmann, Bernd
dc.contributor.department Statistics
dc.contributor.majorProfessor Huaiqing Wu
dc.date 2021-01-08T14:51:11.000
dc.date.accessioned 2021-02-25T00:03:33Z
dc.date.available 2021-02-25T00:03:33Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.embargo 2023-01-08
dc.date.issued 2020-01-01
dc.description.abstract <p>This work introduces two novel algorithms for multivariate regression: a partial least squares (PLS) variable selection method based on resampling and a PLS method using data transformation of the PLS weights (twPLS). The algorithms are tested on three spectral datasets (near-infrared and Raman) by predicting univariate response variables. The results are compared with the predictions of three other established methods comprising standard PLS, variable selection by sparse PLS (SPLS) and variable selection by variable importance in projection (VIP).<br />Compared with the standard PLS method, the novel algorithms clearly improve predictions for one dataset and show slightly more accurate predictions for two other datasets.<br />The two novel algorithms show comparable results with the SPLS and variable selection by VIP for all three datasets. These results demonstrate the effectiveness of the new approaches.</p>
dc.format.mimetype PDF
dc.identifier archive/lib.dr.iastate.edu/creativecomponents/642/
dc.identifier.articleid 1731
dc.identifier.contextkey 20387232
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/642
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/93762
dc.source.bitstream archive/lib.dr.iastate.edu/creativecomponents/642/CC_Fuhrmann_upload.pdf|||Sat Jan 15 01:22:30 UTC 2022
dc.subject.disciplines Applied Statistics
dc.subject.keywords PLS
dc.subject.keywords partial least squares
dc.subject.keywords chemometrics
dc.subject.keywords variable selection
dc.title Application of Two Novel Partial Least Squares-Based Regression Methods to the Analysis of Spectral Datasets
dc.type article
dc.type.genre creativecomponent
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.discipline Statistics
thesis.degree.discipline Statistics
thesis.degree.level creativecomponent
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