Show simple item record

dc.contributor.authorKhinkis, L.
dc.contributor.authorCrotzer M.
dc.contributor.authorOprisan, A.
dc.date.accessioned2019-01-02T13:23:44Z
dc.date.available2019-01-02T13:23:44Z
dc.date.issued2018cs
dc.identifier.citationMathematics for Applications. 2018 vol. 7, č. 1, s. 41-52. ISSN 1805-3629cs
dc.identifier.issn1805-3629
dc.identifier.urihttp://hdl.handle.net/11012/137267
dc.description.abstractIn nonlinear regression analysis, the residual sum of squares may possess multiple local minima. This complicates finding the global minimum and adversely affects reliability of the relevant statistical methods. Identifying and sizing up the regions of a readily identifiable global minimum (RIGM) is therefore of both theo- retical and practical interest. This paper addresses the issue by using equidistant function previously introduced by the first two co-authors of this paper.en
dc.formattextcs
dc.format.extent41-52cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematikycs
dc.relation.ispartofMathematics for Applicationsen
dc.relation.urihttp://ma.fme.vutbr.cz/archiv/7_1/ma_7_1_4_khinkis_et_al_final.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematikycs
dc.subjectnonlinear regression, parameter estimation, residual sum of squaresen
dc.titleSizing up the regions of unique minima in the least squares nonlinear regressionen
eprints.affiliatedInstitution.departmentÚstav matematikycs
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
dc.coverage.issue1cs
dc.coverage.volume7cs
dc.identifier.doi10.13164/ma.2018.04en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record