118 citations · 1.3k across the 129 of their papers we have counts for
13 papers · 1 filter
Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
Joshua Agterberg, Zachary Lubberts, Carey Priebe
We propose an estimator for the singular vectors of high-dimensional low-rank matrices corrupted by additive subgaussian noise, where the noise matrix is allowed to have dependence…
On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models
Joshua Agterberg, Minh Tang, Carey E. Priebe
Two separate and distinct sources of nonidentifiability arise naturally in the context of latent position random graph models, though neither are unique to this setting. In this pa…
On a complete and sufficient statistic for the correlated Bernoulli random graph model
Donniell E. Fishkind, Avanti Athreya, Lingyao Meng +2
Inference on vertex-aligned graphs is of wide theoretical and practical importance.There are, however, few flexible and tractable statistical models for correlated graphs, and even…
The exact form of the 'Ockham factor' in model selection
Jonathan Rougier, Carey Priebe
We explore the arguments for maximizing the `evidence' as an algorithm for model selection. We show, using a new definition of model complexity which we term `flexibility', that ma…
Approximate Information Tests on Statistical Submanifolds
Michael W. Trosset, Carey E. Priebe
Parametric inference posits a statistical model that is a specified family of probability distributions. Restricted inference, e.g., restricted likelihood ratio testing, attempts t…
On spectral embedding performance and elucidating network structure in stochastic block model graphs
Joshua Cape, Minh Tang, Carey E. Priebe
Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations, such as the graph Laplacian or adja…