Bias-Variance Tradeoffs in Joint Spectral Embeddings

Wednesday, September 16, 2020 - 10:40am - 11:25am
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.