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dc.contributor.authorChen, LAen_US
dc.contributor.authorChan, WYen_US
dc.contributor.authorLee, TSen_US
dc.date.accessioned2019-04-02T05:59:32Z-
dc.date.available2019-04-02T05:59:32Z-
dc.date.issued1997-01-01en_US
dc.identifier.issn0361-0926en_US
dc.identifier.urihttp://dx.doi.org/10.1080/03610929708832036en_US
dc.identifier.urihttp://hdl.handle.net/11536/149638-
dc.description.abstractThis paper presents a unified study of smoothing tensor product piecewise polynomials as tensor product polynomial splines. This study generalizes the theory of univariate polynomial splines of Poirier (1973) and Smith (1979) to the multivariate setting, and the method proposed herein provides various spline bases that gives great flexibility for selecting models. An example analyzing an Australia wine industry data shows simple tensor product spline can capture the trend in the data appropriately to law of economics whereas multiple lineal model fails in capturing the trend (see Maddala (1988)).en_US
dc.language.isoen_USen_US
dc.subjectnonparametric regressionen_US
dc.subjectpiecewise polynomialsen_US
dc.subjecttensor product splineen_US
dc.titleTensor product polynomial splinesen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/03610929708832036en_US
dc.identifier.journalCOMMUNICATIONS IN STATISTICS-THEORY AND METHODSen_US
dc.citation.volume26en_US
dc.citation.spage2093en_US
dc.citation.epage2111en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:A1997XV59000003en_US
dc.citation.woscount0en_US
Appears in Collections:Articles