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Penalized nonparametric scalar-on-function regression via principal coordinates
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dc.contributor.author | Reiss, Philip T. | |
dc.contributor.author | Miller, David L. | |
dc.contributor.author | Wu, Pei Shien | |
dc.contributor.author | Hua, Wen Yu | |
dc.date.accessioned | 2018-02-03T00:31:45Z | |
dc.date.available | 2018-02-03T00:31:45Z | |
dc.date.issued | 2017 | |
dc.identifier | 250174746 | |
dc.identifier | 5acab15d-5033-40f1-bc15-f8544b868fc0 | |
dc.identifier | 85017476710 | |
dc.identifier | 000410916600010 | |
dc.identifier.citation | Reiss , 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 . https://doi.org/10.1080/10618600.2016.1217227 | en |
dc.identifier.issn | 1061-8600 | |
dc.identifier.uri | https://hdl.handle.net/10023/12663 | |
dc.description | Philip 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.abstract | A 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. | |
dc.format.extent | 10 | |
dc.format.extent | 1442521 | |
dc.language.iso | eng | |
dc.relation.ispartof | Journal of Computational and Graphical Statistics | en |
dc.subject | Dynamic time warping | en |
dc.subject | Functional regression | en |
dc.subject | Generalized additive model | en |
dc.subject | Kernel ridge regression | en |
dc.subject | Multidimensional scaling | en |
dc.subject | QA Mathematics | en |
dc.subject | Statistics, Probability and Uncertainty | en |
dc.subject | Discrete Mathematics and Combinatorics | en |
dc.subject | Statistics and Probability | en |
dc.subject | DAS | en |
dc.subject.lcc | QA | en |
dc.title | Penalized nonparametric scalar-on-function regression via principal coordinates | en |
dc.type | Journal article | en |
dc.contributor.institution | University of St Andrews. School of Mathematics and Statistics | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Ecological & Environmental Modelling | en |
dc.identifier.doi | 10.1080/10618600.2016.1217227 | |
dc.description.status | Peer reviewed | en |
dc.date.embargoedUntil | 2018-02-02 | |
dc.identifier.url | http://www.tandfonline.com/doi/full/10.1080/10618600.2016.1217227#supplemental-material-section | en |
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