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dc.contributor.authorReiss, Philip T.
dc.contributor.authorMiller, David L.
dc.contributor.authorWu, Pei Shien
dc.contributor.authorHua, Wen Yu
dc.date.accessioned2018-02-03T00:31:45Z
dc.date.available2018-02-03T00:31:45Z
dc.date.issued2017
dc.identifier.citationReiss , P T , Miller , D L , Wu , P S & Hua , W Y 2017 , ' Penalized nonparametric scalar-on-function regression via principal coordinates ' Journal of Computational and Graphical Statistics , vol 26 , no. 3 , pp. 569-587 . DOI: 10.1080/10618600.2016.1217227en
dc.identifier.issn1061-8600
dc.identifier.otherPURE: 250174746
dc.identifier.otherPURE UUID: 5acab15d-5033-40f1-bc15-f8544b868fc0
dc.identifier.otherScopus: 85017476710
dc.identifier.urihttp://hdl.handle.net/10023/12663
dc.identifier.urihttp://www.tandfonline.com/doi/full/10.1080/10618600.2016.1217227#supplemental-material-sectionen
dc.descriptionPhilip Reiss, Pei-Shien Wu, and Wen-Yu Hua gratefully acknowledge the support of the U.S. National Institute of Mental Health (grant 1R01MH095836-01A1).en
dc.description.abstractA number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This article introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. In the proposed method, which we call principal coordinate ridge regression, one regresses the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, principal coordinate ridge regression, with dynamic time warping distance used to define the principal coordinates, is shown to outperform a functional generalized linear model. Supplementary materials for this article are available online.en
dc.format.extent10en
dc.language.isoeng
dc.relation.ispartofJournal of Computational and Graphical Statisticsen
dc.rights© 2017, American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.tandfonline.com / http://dx.doi.org/10.1080/10618600.2016.1217227en
dc.subjectDynamic time warpingen
dc.subjectFunctional regressionen
dc.subjectGeneralized additive modelen
dc.subjectKernel ridge regressionen
dc.subjectMultidimensional scalingen
dc.subjectQA Mathematicsen
dc.subjectStatistics, Probability and Uncertaintyen
dc.subjectDiscrete Mathematics and Combinatoricsen
dc.subjectStatistics and Probabilityen
dc.subjectDASen
dc.subject.lccQAen
dc.titlePenalized nonparametric scalar-on-function regression via principal coordinatesen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.1080/10618600.2016.1217227
dc.description.statusPeer revieweden
dc.date.embargoedUntil02-02-20


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