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A convergent algorithm for a generalized multivariate isotonic regression problem

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dc.contributor.author Hansohm, Juergen
dc.contributor.author Hu, Xiaomi
dc.date.accessioned 2012-06-15T20:22:23Z
dc.date.available 2012-06-15T20:22:23Z
dc.date.issued 2012-02
dc.identifier.citation Hansohm, 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.issn 0932-5026
dc.identifier.issn 1613-9798
dc.identifier.uri http://hdl.handle.net/10057/5132
dc.identifier.uri http://dx.doi.org/10.1007/s00362-010-0317-6
dc.description Click on the DOI link below to access the article (may not be free). en_US
dc.description.abstract Sasabuchi 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.iso en_US en_US
dc.publisher Springer en_US
dc.relation.ispartofseries Statistical Papers;2012:, v.53, no.1
dc.subject Multivariate isotonic regression en_US
dc.subject Projection en_US
dc.subject Dykstra's algorithm en_US
dc.subject Partial order en_US
dc.subject Least squares solution en_US
dc.title A convergent algorithm for a generalized multivariate isotonic regression problem en_US
dc.type Article en_US
dc.description.version Peer reviewed
dc.rights.holder © Springer-Verlag 2010

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