STATISTICS COLLOQUIUM
Zongming Ma, Assoc. Professor
Department of Statistics
The Wharton School
University of Pennsylvania
Optimal hypothesis testing for stochastic block
models with growing degrees
Abstract
In this talk, we discuss optimal hypothesis testing for distinguishing a stochastic block model from an Erdos-Renyi random graph. We derive central limit theorems for a collection of linear spectral statistics under both the null and local alternatives. In addition, we show that linear spectral statistics based on Chebyshev polynomials can be used to approximate signed cycles of growing lengths which in turn determine the likelihood ratio test asymptotically when the graph size and the average degree grow to infinity together. Therefore, one achieves sharp asymptotic optimal power of the testing problem within polynomial time complexity.
DATE: Wednesday, October 18, 2017
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu