- Stochastics Seminar
- Thursday, November 5, 2020 - 15:30 for 1 hour (actually 50 minutes)
- Daniel Sussman – Boston University
We consider the ramifications of utilizing biased latent position estimates in subsequent statistical analysis in exchange for sizable variance reductions in finite networks. We establish an explicit bias-variance tradeoff for latent position estimates produced by the omnibus embedding in the presence of heterogeneous network data. We reveal an analytic bias expression, derive a uniform concentration bound on the residual term, and prove a central limit theorem characterizing the distributional properties of these estimates.
Link to the BlueJeans meeting https://bluejeans.com/974631214