A Short Introduction to Sufficient Dimension Reduction Methods

Date & time

2.30–3.30pm 10 May 2017


John Dedman Building 27 Room G35


Jingyue Lu (ANU/MSI)

Event series


 Robert Culling

With technological advances, datasets have grown in both size and complexity.  One consequence of increasing amounts of data is that we often need to relate a response variable to a potentially large number of possible covariates.   The high dimension of the covariate space makes it difficult to uncover this relationship. Sufficient dimension reduction methods are based on finding a small number of linear combinations of covariates to relate to the response variable. 

In this talk, I am going to give a short introduction to sufficient dimension reduction.  I will start by introducing the main ideas of sufficient dimension reduction and the abstract mathematical problem it is trying to solve.  After that, I will focus on one or two particular sufficient dimension methods to illustrate how sufficient dimension reduction methods works. 

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