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Joint estimation of monotone curves via functional principal component analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, Yei Eun | - |
dc.contributor.author | Zhou, Lan | - |
dc.contributor.author | Ding, Yu | - |
dc.date.accessioned | 2024-04-29T01:01:31Z | - |
dc.date.available | 2024-04-29T01:01:31Z | - |
dc.date.created | 2022-10-24 | - |
dc.date.created | 2022-10-24 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.citation | Computational Statistics and Data Analysis, Vol.166, p. 107343 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | https://hdl.handle.net/10371/199902 | - |
dc.description.abstract | A functional data approach is developed to jointly estimate a collection of monotone curves that are irregularly and possibly sparsely observed with noise. In this approach, the unconstrained relative curvature curves instead of the monotone-constrained functions are directly modeled. Functional principal components are used to describe the major modes of variations of curves and allow borrowing strength across curves for improved estimation. A two-step approach and an integrated approach are considered for model fitting. The simulation study shows that the integrated approach is more efficient than separate curve estimation and the two-step approach. The integrated approach also provides more interpretable principle component functions in an application of estimating weekly wind power curves of a wind turbine. (C) 2021 Published by Elsevier B.V. | - |
dc.language | 영어 | - |
dc.publisher | Elsevier BV | - |
dc.title | Joint estimation of monotone curves via functional principal component analysis | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.csda.2021.107343 | - |
dc.citation.journaltitle | Computational Statistics and Data Analysis | - |
dc.identifier.wosid | 000704175800002 | - |
dc.identifier.scopusid | 2-s2.0-85114936189 | - |
dc.citation.startpage | 107343 | - |
dc.citation.volume | 166 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Shin, Yei Eun | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | NONPARAMETRIC REGRESSION | - |
dc.subject.keywordPlus | MAXIMUM-LIKELIHOOD | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordAuthor | B-splines | - |
dc.subject.keywordAuthor | Functional data analysis | - |
dc.subject.keywordAuthor | Monotone smoothing | - |
dc.subject.keywordAuthor | Penalization | - |
dc.subject.keywordAuthor | Relative curvature function | - |
dc.subject.keywordAuthor | Spline smoothing | - |
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