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Abstract Details
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Activity Number:
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310
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Graphics
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| Abstract - #301790 |
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Title:
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Detection of Central Dimension-Reduction Subspaces in Regression
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Author(s):
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Santiago Velilla*+
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Companies:
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Universidad Carlos III de Madrid
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Address:
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DEPARTAMENTO DE ESTADÍSTICA, GETAFE (MADRID), International, 28903, SPAIN
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Keywords:
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Dimension reduction ;
Graphical regression ;
SIR and SAVE
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Abstract:
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Dimension reduction is a widely applied technique in regression. The basic problem in this field is the description of the central subspace (Cook, 1998), a linear manifold that helps to describe parsimoniously how the conditional distribution of a response variable changes with the values of a set of predictors. However, methods for searching directions inside the central subspace concentrate typically on a portion of it, imposing at the same time some assumptions on the marginal distribution of the regressors. Proposals for an exhaustive characterization of the central subspace exist, but they still depend on restricting the distribution of the regressors. This comunication presents a method for fully recovering the central subspace that places no restrictions on the predictors, other than the existence of first and second order moments. A data example is analyzed.
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