Ping Ma (University of Georgia)
Large samples have been generated routinely from various sources. Classic statistical and analytical methods are not well equipped to analyze such large samples due to expensive computational costs. In this talk, I will present an asympirical (asymptotic + empirical) analysis in large samples. The proposed method can significantly reduce computational costs in high-dimensional and large-scale data. We show the estimator based on the proposed methods achieves the optimal convergence rate. Extensive simulation studies will be presented to demonstrate numerical advantages of our method over competing methods. I will further illustrate the empirical performance of the proposed approach using two real data examples.