Use of the mean quadratic error of prediction for the construction of biased linear models

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dc.contributor.authorMilitký, Jiří
dc.contributor.authorMeloun, Milan
dc.date.accessioned2016-07-08
dc.date.available2016-07-08
dc.date.issued1993
dc.description.abstractThe main practical problems caused by multi-collinearity are reviewed. The biased estimators based on the generalization of principal components for avoiding multi-collinearity problems are described. The mean quadratic error of prediction criterion is used for the selection of suitable bias. Some advantages of biased regression are demostrated on the problem of intercept estimation in a polynomial model. © 1993.en
dc.formattext
dc.identifier.doi10.1016/0003-2670(93)80439-R
dc.identifier.issn0003-2670
dc.identifier.scopus2-s2.0-0027316326
dc.identifier.urihttps://dspace.tul.cz/handle/15240/16569
dc.language.isoen
dc.publisherElsevier Science Bv
dc.relation.ispartofAnalytica Chimica Acta
dc.sourcej-scopus
dc.sourcej-wok
dc.subjectBiased linear modelsen
dc.subjectMean quadratic error of predictionen
dc.subjectMulti-collinearityen
dc.subjectOptimization methodsen
dc.titleUse of the mean quadratic error of prediction for the construction of biased linear modelsen
dc.typearticle
local.accessaccess
local.citation.epage271
local.citation.spage267
local.departmentDepartment of Textile Materials
local.facultyFaculty of Textile Engineering
local.identifier.codenACACA
local.identifier.wokA1993LE81000011
local.notenefunguje RIV
local.relation.issue2
local.relation.volume277
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