text

# Multivariate isotonic regression and its algorithms

## SOAR Repository

 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 en_US 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. 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