Some Recent Developments in
Sufficient Dimension Reduction
Sufficient dimension reduction can aid the analysis of high-dimensional data by transforming the problems to low dimensional projections. The curse of dimensionality is often alleviated, and the informative data visualization may be enabled. In this talk, we start with introducing some recent challenges to the methodology of sufficient dimension reduction, including small-n-large-p regressions, simultaneous variable selection along with dimension reduction, and missing data in the predictors. We next continue the talk with a discussion of some recently proposed dimension reduction methods to address the above challenges.