Efficiency of the OLSE for regressions on two-dimensional grids with sinusoidal regressors and spatially correlated errors SCIE SCOPUS

DC Field Value Language
dc.contributor.author Shin, DW -
dc.contributor.author Kim, DG -
dc.contributor.author Kim, HJ -
dc.date.accessioned 2020-04-21T07:25:47Z -
dc.date.available 2020-04-21T07:25:47Z -
dc.date.created 2020-01-28 -
dc.date.issued 2002 -
dc.identifier.issn 0026-1335 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/5796 -
dc.description.abstract For spatial regressions with sinusoidal surfaces, the ordinary least squares estimator (OLSE) is shown to be asymptotically as efficient as the generalized least squares estimator (GLSE) in that the covariance matrices of the two estimators have the same nontrivial limit under the same normalization. -
dc.description.uri 1 -
dc.language English -
dc.publisher PHYSICA-VERLAG GMBH & CO -
dc.subject ORDINARY LEAST-SQUARES -
dc.subject MODEL -
dc.title Efficiency of the OLSE for regressions on two-dimensional grids with sinusoidal regressors and spatially correlated errors -
dc.type Article -
dc.citation.endPage 258 -
dc.citation.startPage 247 -
dc.citation.title METRIKA -
dc.citation.volume 56 -
dc.citation.number 3 -
dc.contributor.alternativeName 김한준 -
dc.identifier.bibliographicCitation METRIKA, v.56, no.3, pp.247 - 258 -
dc.identifier.doi 10.1007/s001840100177 -
dc.identifier.scopusid 2-s2.0-0036459221 -
dc.identifier.wosid 000179879000006 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.subject.keywordPlus ORDINARY LEAST-SQUARES -
dc.subject.keywordPlus MODEL -
dc.subject.keywordAuthor efficiency -
dc.subject.keywordAuthor GLSE -
dc.subject.keywordAuthor OLSE -
dc.subject.keywordAuthor sinusoidal surface -
dc.subject.keywordAuthor spatial regression -
dc.relation.journalWebOfScienceCategory Statistics & Probability -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Mathematics -
Appears in Collections:
Ocean Climate Solutions Research Division > Ocean Climate Response & Ecosystem Research Department > 1. Journal Articles
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