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.