W&M Homepage

Location

Abstract: High dimensional data appear in many research fields, such as marketing, finance, environmental and medical studies.  Sufficient dimension reduction is a useful tool to tackle this challenging problem. In this talk, a new formulation is proposed based on the Hellinger integral of order two, introduced as a natural measure of the regression information contained in a predictor subspace. The link between chi-squared divergence and dimension reduction subspaces is the key to our approach, which requires minimal (essentially, just existence) assumptions and is computationally efficient relative to existing methods. Numerical studies and a real data example will be presented.

Contact

GuanNan Wang

This Event Appears On

• W&M Featured Events
• Arts & Sciences Events
• Mathematics Events