| The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics J Cape, M Tang, CE Priebe Annals of Statistics 47 (5), 2405-2439, 2019 | 179 | 2019 |
| A statistical interpretation of spectral embedding: the generalised random dot product graph P Rubin-Delanchy, J Cape, M Tang, CE Priebe Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2022 | 174 | 2022 |
| Inference for multiple heterogeneous networks with a common invariant subspace J Arroyo, A Athreya, J Cape, G Chen, CE Priebe, JT Vogelstein Journal of Machine Learning Research 22 (142), 1-49, 2021 | 142 | 2021 |
| On a two-truths phenomenon in spectral graph clustering CE Priebe, Y Park, JT Vogelstein, JM Conroy, V Lyzinski, M Tang, ... Proceedings of the National Academy of Sciences 116 (13), 5995-6000, 2019 | 97 | 2019 |
| Signal-plus-noise matrix models: eigenvector deviations and fluctuations J Cape, M Tang, CE Priebe Biometrika 106 (1), 243-250, 2019 | 74 | 2019 |
| Asymptotically efficient estimators for stochastic blockmodels: The naive MLE, the rank-constrained MLE, and the spectral estimator M Tang, J Cape, CE Priebe Bernoulli 28 (2), 1049-1073, 2022 | 46* | 2022 |
| Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data E Held, J Cape, N Tintle BMC proceedings 10, 141-145, 2016 | 44 | 2016 |
| Spectral estimation of large stochastic blockmodels with discrete nodal covariates A Mele, L Hao, J Cape, CE Priebe Journal of Business & Economic Statistics 41 (4), 1364-1376, 2023 | 36* | 2023 |
| On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs J Cape, M Tang, CE Priebe Network Science 7 (3), 269-291, 2019 | 26 | 2019 |
| Structural covariance networks in schizophrenia: a systematic review Part I K Prasad, J Rubin, A Mitra, M Lewis, N Theis, B Muldoon, S Iyengar, ... Schizophrenia research 240, 1-21, 2022 | 24 | 2022 |
| Eigenvalues of stochastic blockmodel graphs and random graphs with low-rank edge probability matrices A Athreya, J Cape, M Tang Sankhya A, 1-28, 2022 | 22* | 2022 |
| Bayesian sparse spiked covariance model with a continuous matrix shrinkage prior F Xie, J Cape, CE Priebe, Y Xu Bayesian Analysis 17 (4), 1193-1217, 2022 | 21* | 2022 |
| On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates C Mu, A Mele, L Hao, J Cape, A Athreya, CE Priebe IEEE Transactions on Network Science and Engineering 9 (5), 3373--3384, 2022 | 21 | 2022 |
| Structural covariance networks in schizophrenia: A systematic review Part II K Prasad, J Rubin, A Mitra, M Lewis, N Theis, B Muldoon, S Iyengar, ... Schizophrenia research 239, 176-191, 2022 | 18 | 2022 |
| Multiple network embedding for anomaly detection in time series of graphs G Chen, J Arroyo, A Athreya, J Cape, JT Vogelstein, Y Park, C White, ... Computational Statistics & Data Analysis 203, 108070, 2025 | 17 | 2025 |
| A note on the orthogonal Procrustes problem and norm-dependent optimality J Cape The Electronic Journal of Linear Algebra 36 (36), 158-168, 2020 | 14* | 2020 |
| The Kato–Temple inequality and eigenvalue concentration with applications to graph inference J Cape, M Tang, CE Priebe Electronic Journal of Statistics 11 (2), 3954-3978, 2017 | 13 | 2017 |
| Threshold Selection for Brain Connectomes N Theis, J Rubin, J Cape, S Iyengar, KM Prasad Brain Connectivity 13 (7), 383-393, 2023 | 11* | 2023 |
| Estimating Heston's and Bates’ models parameters using Markov chain Monte Carlo simulation J Cape, W Dearden, W Gamber, J Liebner, Q Lu, ML Nguyen Journal of Statistical Computation and Simulation 85 (11), 2295-2314, 2015 | 10 | 2015 |
| Symplectic reduction at zero angular momentum J Cape, HC Herbig, C Seaton Journal of Geometric Mechanics 8 (1), 13-34, 2016 | 8 | 2016 |
Carey E. PriebeProfessor of Applied Mathematics and Statistics, Johns Hopkins UniversityVerified email at jhu.edu
