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

Add to your list(s) Download to your calendar using vCal

• Jérôme Saracco, Bordeaux Institute of Technology & Bordeaux Institute of Mathematics (Probability and Statistics team)
• Tuesday 20 November 2012, 14:30-15:30
• Large Seminar Room, 1st Floor, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge.

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.

This talk is included in these lists:

• All Talks (aka the CURE list)
• BHRU Annual Lecture 2015
• BHRU Annual Lecture 2016
• Biology
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Immunology
• Cambridge talks
• Cardiovascular Epidemiology Unit Special Seminars
• Chris Davis' list
• Department of Public Health and Primary Care
• Graduate-Seminars
• Interested Talks
• Large Seminar Room, 1st Floor, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge
• Life Sciences
• Life Sciences
• ME Seminar
• MRC Biostatistics Unit Seminars
• Primary Care
• PublicHealth@Cambridge
• Trust & Technology Initiative - interesting events
• bld31
• my_list
• ndk22's list
• ob366-ai4er
• other talks
• primary care
• rp587

Note that ex-directory lists are not shown.