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Jérôme Saracco, Bordeaux Institute of Technology & Bordeaux Institute of Mathematics (Probability and Statistics team)
Tuesday 20 November 2012, 14:30-15:30
Dimension reduction based on sliced inverse regression (SIR): a look at the special case when n ‹p
- 👤 Speaker: Jérôme Saracco, Bordeaux Institute of Technology & Bordeaux Institute of Mathematics (Probability and Statistics team)
- 📅 Date & Time: Tuesday 20 November 2012, 14:30 - 15:30
- 📍 Venue: Large Seminar Room, 1st Floor, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge
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Dr Jack Bowden
Abstract
In this talk, we first give an overview of sliced inverse regression (SIR) which is an attractive dimension-reduction approach to model the effect of the p-dimensional covariates x on y via a semi-parametric regression model. Several authors proposed and studied SIR -based methods when the sample size n is greater than p. These approaches do not work when n‹p since they are based on the inversion of the variance matrix of the covariate x. Then, we present some procedures to tackle this issue and we compare them on simulated data.
Series This talk is part of the MRC Biostatistics Unit Seminars series.
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