Matches in SemOpenAlex for { <https://semopenalex.org/work/W2290709430> ?p ?o ?g. }
Showing items 1 to 98 of
98
with 100 items per page.
- W2290709430 endingPage "A411" @default.
- W2290709430 startingPage "A385" @default.
- W2290709430 abstract "In this paper we present a quasi-optimal sample set for ordinary least squares (OLS) regression. The quasi-optimal set is designed in such a way that, for a given number of samples, it can deliver the regression result as close as possible to the result obtained by a (much) larger set of candidate samples. The quasi-optimal set is determined by maximizing a quantity measuring the mutual column orthogonality and the determinant of the model matrix. This procedure is nonadaptive, in the sense that it does not depend on the sample data. This is useful in practice, as it allows one to determine, prior to the potentially expensive data collection procedure, where to sample the underlying system. In addition to presenting the theoretical motivation of the quasi-optimal set, we also present its efficient implementation via a greedy algorithm, along with several numerical examples to demonstrate its efficacy. Since the quasi-optimal set allows one to obtain a near optimal regression result under any affordable number of samples, it notably outperforms other standard choices of sample sets. An immediate application of the quasi-optimal set is stochastic collocation for uncertainty quantification, where data collection usually requires expensive PDE solutions. It is demonstrated that the quasi-optimal set OLS can deliver very competitive results, compared to the generalized polynomial chaos Galerkin method, and yet it remains fully nonintrusive and is much more flexible for practical computations." @default.
- W2290709430 created "2016-06-24" @default.
- W2290709430 creator A5023632213 @default.
- W2290709430 creator A5030218917 @default.
- W2290709430 date "2016-01-01" @default.
- W2290709430 modified "2023-10-18" @default.
- W2290709430 title "Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression" @default.
- W2290709430 cites W1980029630 @default.
- W2290709430 cites W1983156129 @default.
- W2290709430 cites W1984320340 @default.
- W2290709430 cites W2014910287 @default.
- W2290709430 cites W2018159038 @default.
- W2290709430 cites W2023427312 @default.
- W2290709430 cites W2026679679 @default.
- W2290709430 cites W2036242000 @default.
- W2290709430 cites W2047405057 @default.
- W2290709430 cites W2051307541 @default.
- W2290709430 cites W2056558085 @default.
- W2290709430 cites W2065120373 @default.
- W2290709430 cites W2070335948 @default.
- W2290709430 cites W2076598621 @default.
- W2290709430 cites W2105596596 @default.
- W2290709430 cites W2140951396 @default.
- W2290709430 cites W2143591652 @default.
- W2290709430 cites W2146552914 @default.
- W2290709430 cites W2171971725 @default.
- W2290709430 cites W2471423265 @default.
- W2290709430 cites W2963727569 @default.
- W2290709430 doi "https://doi.org/10.1137/15m1015868" @default.
- W2290709430 hasPublicationYear "2016" @default.
- W2290709430 type Work @default.
- W2290709430 sameAs 2290709430 @default.
- W2290709430 citedByCount "38" @default.
- W2290709430 countsByYear W22907094302016 @default.
- W2290709430 countsByYear W22907094302017 @default.
- W2290709430 countsByYear W22907094302018 @default.
- W2290709430 countsByYear W22907094302019 @default.
- W2290709430 countsByYear W22907094302020 @default.
- W2290709430 countsByYear W22907094302021 @default.
- W2290709430 countsByYear W22907094302022 @default.
- W2290709430 countsByYear W22907094302023 @default.
- W2290709430 crossrefType "journal-article" @default.
- W2290709430 hasAuthorship W2290709430A5023632213 @default.
- W2290709430 hasAuthorship W2290709430A5030218917 @default.
- W2290709430 hasConcept C105795698 @default.
- W2290709430 hasConcept C120068334 @default.
- W2290709430 hasConcept C126255220 @default.
- W2290709430 hasConcept C152877465 @default.
- W2290709430 hasConcept C169241690 @default.
- W2290709430 hasConcept C177264268 @default.
- W2290709430 hasConcept C185592680 @default.
- W2290709430 hasConcept C198531522 @default.
- W2290709430 hasConcept C199360897 @default.
- W2290709430 hasConcept C28826006 @default.
- W2290709430 hasConcept C33923547 @default.
- W2290709430 hasConcept C41008148 @default.
- W2290709430 hasConcept C43617362 @default.
- W2290709430 hasConcept C51823790 @default.
- W2290709430 hasConcept C99656134 @default.
- W2290709430 hasConceptScore W2290709430C105795698 @default.
- W2290709430 hasConceptScore W2290709430C120068334 @default.
- W2290709430 hasConceptScore W2290709430C126255220 @default.
- W2290709430 hasConceptScore W2290709430C152877465 @default.
- W2290709430 hasConceptScore W2290709430C169241690 @default.
- W2290709430 hasConceptScore W2290709430C177264268 @default.
- W2290709430 hasConceptScore W2290709430C185592680 @default.
- W2290709430 hasConceptScore W2290709430C198531522 @default.
- W2290709430 hasConceptScore W2290709430C199360897 @default.
- W2290709430 hasConceptScore W2290709430C28826006 @default.
- W2290709430 hasConceptScore W2290709430C33923547 @default.
- W2290709430 hasConceptScore W2290709430C41008148 @default.
- W2290709430 hasConceptScore W2290709430C43617362 @default.
- W2290709430 hasConceptScore W2290709430C51823790 @default.
- W2290709430 hasConceptScore W2290709430C99656134 @default.
- W2290709430 hasFunder F4320306076 @default.
- W2290709430 hasFunder F4320306084 @default.
- W2290709430 hasFunder F4320338279 @default.
- W2290709430 hasIssue "1" @default.
- W2290709430 hasLocation W22907094301 @default.
- W2290709430 hasOpenAccess W2290709430 @default.
- W2290709430 hasPrimaryLocation W22907094301 @default.
- W2290709430 hasRelatedWork W2020471417 @default.
- W2290709430 hasRelatedWork W2023475031 @default.
- W2290709430 hasRelatedWork W2075634219 @default.
- W2290709430 hasRelatedWork W2124354819 @default.
- W2290709430 hasRelatedWork W2273635254 @default.
- W2290709430 hasRelatedWork W2347528012 @default.
- W2290709430 hasRelatedWork W2373426579 @default.
- W2290709430 hasRelatedWork W2801807510 @default.
- W2290709430 hasRelatedWork W3179776907 @default.
- W2290709430 hasRelatedWork W4312627304 @default.
- W2290709430 hasVolume "38" @default.
- W2290709430 isParatext "false" @default.
- W2290709430 isRetracted "false" @default.
- W2290709430 magId "2290709430" @default.
- W2290709430 workType "article" @default.