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Multivariate isotonic regression and its algorithms

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dc.contributor.author Hoffmann, Linda en_US
dc.contributor.author Hu, Xiaomi en_US
dc.date.accessioned 2009-11-19T21:48:48Z
dc.date.available 2009-11-19T21:48:48Z
dc.date.issued 2009-05-01 en_US
dc.identifier.citation Hoffmann, Linda and Xiaomi Hu(2009) . Multivariate Isotonic Regression and Its Algorithms. In Proceedings: 5th Annual Symposium: Graduate Research and Scholarly Projects. Wichita, KS: Wichita State University, p. 38-39 en_US
dc.identifier.uri http://hdl.handle.net/10057/2324
dc.description Paper presented to the 5th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Hughes Metropolitan Complex, Wichita State University, May 1, 2009. en_US
dc.description Research completed at the Department of Mathematics & Statistics, College of Liberal Arts & Sciences en_US
dc.description.abstract We use regression functions, which are the means of random variables, to interpret statistical inference. Often an order is imposed on the values of the regression function. Thus, we refer to the regression as an order restricted regression or an isotonic regression. In this paper we explain how to calculate multivariate isotonic regression. However, we investigate the case for a particular restriction on our elements. We impose relations between elements of the same row but not between rows. The technique is to decompose our multivariate model into univariate models so that prior knowledge about the simpler case can be used. Finally, we propose an algorithm to calculate multivariate isotonic regression. This algorithm could then be converted into a computer program. en_US
dc.format.extent 163586 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.publisher Wichita State University. Graduate School en_US
dc.relation.ispartofseries GRASP en_US
dc.relation.ispartofseries v.5 en_US
dc.title Multivariate isotonic regression and its algorithms en_US
dc.type Conference paper en_US

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