Texas A&M - Labors of Lab
This installment of "Labors of Lab" features statistics Ph.D candidate Richard Payne '17, who is using Bayesian statistics -- specifically big-data classification methods -- in collaboration with Texas A&M distinguished professor of statistics Bani Mallick to make algorithms more efficient.
Our latest Labors of Lab installment features statistics Ph.D. candidate Richard Payne '17, who is using Bayesian statistics -- specifically big-data classification methods -- in collaboration with Texas A&M distinguished professor of statistics Bani Mallick to make algorithms more efficient.Read More →
Texas A&M Statistics is home once again to two of the top five teams in the national finals of the Capital One Modeling Competition, focused this year on search engine marketing. And the winner is . . . Texas A&M, placing first and third to claim its second title in the three-year-old event!Read More →
11:30 AM / 12:20 PM Blocker Building (BLOC), Room 113 979-845-3141
Department of Mathematical Sciences
University of Texas, Dallas
Bootstrap Inference on Degree Distributions of Random Networks
In this talk we discuss a new nonparametric “patchwork” resampling approach to network inference based on the adaptation of “blocking” argument, developed for bootstrapping of time series and re-tiling for spatial data, to random networks. We discuss how the new “patchwork” procedure can be used to quantify estimation uncertainty for network statistics that are functions of degree distribution. We develop a new computationally efficient and data-driven cross-validation algorithm for selecting an optimal “patch” size. We illustrate utility of the new “patchwork” bootstrap procedure for inference on simulated networks, flight and Wikipedia networks.