 A new sliced inverse regression method for multivariate responseR. Coudret, S. Girard and J. Saracco Computational Statistics & Data Analysis, 2014, vol. 77, issue C, 285-299 Abstract: A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional covariate x is considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective dimension reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate n of the estimated EDR space is shown. The choice of the dimension of the EDR space is discussed. Moreover, a way to cluster components of y related to the same EDR space is provided. Thus, the proposed multivariate SIR method can be used properly on each cluster instead of blindly applying it on all components of y. The numerical performances of multivariate SIR are illustrated on a simulation study. An application to the Minneapolis elementary schools data is also provided. Although the proposed methodology relies on SIR, it opens the door for new regression approaches with a multivariate response. They could be built similarly based on other reduction dimension methods. Keywords: Dimension reduction; Semiparametric regression model; Multivariate response; Sliced inverse regression (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:77:y:2014:i:c:p:285-299 DOI: 10.1016/j.csda.2014.03.006 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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