University of Cambridge > Talks.cam > MRC Biostatistics Unit Seminars > Dimension reduction based on sliced inverse regression (SIR): a look at the special case when n ‹p

Dimension reduction based on sliced inverse regression (SIR): a look at the special case when n ‹p

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If you have a question about this talk, please contact Dr Jack Bowden.

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.

This talk is part of the MRC Biostatistics Unit Seminars series.

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