Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A two-sample test for subspace equality in networks uses the Frobenius norm of projection matrix differences, with proven asymptotic normality to Gaussian under logarithmic average degree growth.
citing papers explorer
-
Statistically and Computationally Optimal Estimation and Inference of Common Subspaces
Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
-
Two-Sample Hypothesis Testing for Subspace Equality in Network Data
A two-sample test for subspace equality in networks uses the Frobenius norm of projection matrix differences, with proven asymptotic normality to Gaussian under logarithmic average degree growth.