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dc.contributor.authorChen, LAen_US
dc.date.accessioned2019-04-02T05:58:41Z-
dc.date.available2019-04-02T05:58:41Z-
dc.date.issued1997-11-06en_US
dc.identifier.issn0167-9473en_US
dc.identifier.urihttp://dx.doi.org/10.1016/S0167-9473(97)00018-2en_US
dc.identifier.urihttp://hdl.handle.net/11536/149708-
dc.description.abstractMultivariate regression splines of arbitrary order assuming known knots using the additional function are developed. Model description and possible parameter tests for obtaining regression splines are also stated in detail for the triplicate case. A simulation study of drawn data from the Lubricant nonlinear regression model, where ''Lubricant'' is referred to lubricant data that we use in application, to compare the mean squares errors for the multivariate regression spline models and the multivariate polynomial models show the need of employing the multivariate regression splines in the approximation of the nonlinear regression models. (C) 1997 Elsevier Science B.V.en_US
dc.language.isoen_USen_US
dc.subjectmultivariate regression splineen_US
dc.subjectpiecewise polynomialen_US
dc.titleMultivariate regression splinesen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/S0167-9473(97)00018-2en_US
dc.identifier.journalCOMPUTATIONAL STATISTICS & DATA ANALYSISen_US
dc.citation.volume26en_US
dc.citation.spage71en_US
dc.citation.epage82en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:A1997YL35300005en_US
dc.citation.woscount3en_US
Appears in Collections:Articles