Special Colloquium
Prof. Zuofeng Shang
Binghamton University
Computationally Efficient Nonparametric Testing
Abstract: A recent trend of big data problems is to develop computationally efficient inferential methods
that embed computational thinking into uncertainty quantification. In this talk I will introduce two new
classes of nonparametric testing that scale well with large datasets. One class is based on randomized
sketches which can be implemented in one computer, while the other class requires parallel computing. Our
theoretical contribution is to characterize the minimal computational cost that is needed to achieve
testing optimality. Optimal estimation is a byproduct. The proposed methods are examined by simulated and
real datasets.
Wednesday January 11, 2017 at 3:00 PM in SEO 636