Graduate Student Seminar

Speaker: Dr. Trevor Park

Title: Aspects of Principal Component Rotation

Abstract: Principal component methodology is widely used for many purposes, including exploratory data analysis. However, principal component estimates are subject to high sampling variability, especially for high-dimensional data with low sample sizes. This variability can be reduced by using methods for simplifying the components, including methods based on dimension reduction, thresholding, and penalization. Some recent likelihood-based methods will be described, along with connections to optimization on the Stiefel manifold.

Note: Dr. Park joined our faculty this Fall, as a Visiting Assistant Professor in Statistics.