118 citations · 602 across the 43 of their papers we have counts for
10 papers · 1 filter
Limit results for distributed estimation of invariant subspaces in multiple networks inference and PCA
Runbing Zheng, Minh Tang
Several statistical problems, such as multiple heterogeneous graph analysis, distributed PCA, integrative data analysis, and simultaneous dimension reduction of images, can involve…
Perturbation Analysis of Randomized SVD and its Applications to Statistics
Yichi Zhang, Minh Tang
Randomized singular value decomposition (RSVD) is a class of computationally efficient algorithms for computing the truncated SVD of large data matrices. Given an matr…
Nonparametric two-sample hypothesis testing for low-rank random graphs of differing sizes
Joshua Agterberg, Minh Tang, Carey Priebe
Given two networks of differing sizes, it is of interest to test whether the two networks belong to the same distribution. We formalize the notion of "equality of distribution" und…
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 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…
Signal-plus-noise matrix models: eigenvector deviations and fluctuations
Joshua Cape, Minh Tang, Carey E. Priebe
Estimating eigenvectors and low-dimensional subspaces is of central importance for numerous problems in statistics, computer science, and applied mathematics. This paper characteri…
The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
Joshua Cape, Minh Tang, Carey E. Priebe
The singular value matrix decomposition plays a ubiquitous role throughout statistics and related fields. Myriad applications including clustering, classification, and dimensionali…
The Kato--Temple inequality and eigenvalue concentration with applications to graph inference
Joshua Cape, Minh Tang, Carey E. Priebe
We present an adaptation of the Kato--Temple inequality for bounding perturbations of eigenvalues with applications to statistical inference for random graphs, specifically hypothe…
A nonparametric two-sample hypothesis testing problem for random dot product graphs
Minh Tang, Avanti Athreya, Daniel L. Sussman +2
We consider the problem of testing whether two finite-dimensional random dot product graphs have generating latent positions that are independently drawn from the same distribution…
A central limit theorem for scaled eigenvectors of random dot product graphs
Avanti Athreya, Vince Lyzinski, David J. Marchette +3
We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random dot product graph whose true latent positions…