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dc.contributor.authorHansohm, Juergen
dc.contributor.authorHu, Xiaomi
dc.date.accessioned2012-06-15T20:22:23Z
dc.date.available2012-06-15T20:22:23Z
dc.date.issued2012-02
dc.identifier.citationHansohm, Jèurgen, and Xiaomi Hu. 2012. "A convergent algorithm for a generalized multivariate isotonic regression problem". Statistical Papers. 53 (1): 107-115.en_US
dc.identifier.issn0932-5026
dc.identifier.issn1613-9798
dc.identifier.urihttp://hdl.handle.net/10057/5132
dc.identifier.urihttp://dx.doi.org/10.1007/s00362-010-0317-6
dc.descriptionClick on the DOI link below to access the article (may not be free).en_US
dc.description.abstractSasabuchi et al. (Biometrika 70(2):465-472, 1983) introduces a multivariate version of the well-known univariate isotonic regression which plays a key role in the field of statistical inference under order restrictions. His proposed algorithm for computing the multivariate isotonic regression, however, is guaranteed to converge only under special conditions (Sasabuchi et al., J Stat Comput Simul 73(9):619-641, 2003). In this paper, a more general framework for multivariate isotonic regression is given and an algorithm based on Dykstra's method is used to compute the multivariate isotonic regression. Two numerical examples are given to illustrate the algorithm and to compare the result with the one published by Fernando and Kulatunga (Comput Stat Data Anal 52:702-712, 2007).en_US
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesStatistical Papers;2012:, v.53, no.1
dc.subjectMultivariate isotonic regressionen_US
dc.subjectProjectionen_US
dc.subjectDykstra's algorithmen_US
dc.subjectPartial orderen_US
dc.subjectLeast squares solutionen_US
dc.titleA convergent algorithm for a generalized multivariate isotonic regression problemen_US
dc.typeArticleen_US
dc.description.versionPeer reviewed
dc.rights.holder© Springer-Verlag 2010


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