Research Interests
- Statistical Network Analysis
- High-dimensional Statistics
- Spectral Methods
- Mathematical Data Science
- Nonparametric Statistics
- Nonconvex Optimization
Preprints
- Distributional Theory and Statistical Inference for Linear Functions of Eigenvectors with Small Eigengaps
Joshua Agterberg, 2023.
[arXiv].
- Statistical Inference for Low-Rank Tensors: Heteroskedasticity, Subgaussianity, and Applications
Joshua Agterberg and Anru Zhang, 2023. (Draft available upon request.)
- An Overview of Asymptotic Normality in Stochastic Blockmodels: Cluster Analysis and Inference
Joshua Agterberg and Joshua Cape, 2023.
[arXiv].
- Estimating Higher-Order Mixed Memberships via the $\ell_{2,\infty}$ Tensor Perturbation Bound
Joshua Agterberg and Anru Zhang, 2023.
[arXiv].
- Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
Joshua Agterberg, Zachary Lubberts, and Jesús Arroyo, 2023.
[arXiv].
- Nonparametric Two-Sample Hypothesis Testing for Random Graphs with Negative and Repeated Eigenvalues
Joshua Agterberg, Minh Tang, and Carey Priebe, Submitted, 2020.
(Selected as a finalist for the Nonparametric Statistics Student Competition, JSM 2021.)
(Won best presentation award for the Nonparametric Statistics Student Competition, JSM 2021.)
[arXiv].
- On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models
Joshua Agterberg, Minh Tang, and Carey Priebe, Submitted, 2020.
[arXiv].
Publications
- Correcting a Nonparametric Two-sample Graph Hypothesis Test for Graphs with Different Numbers of
Vertices with Applications to Connectomics
Anton Alyakin, Joshua Agterberg, Hayden Helm, and Carey Priebe,
Applied Network Science, 2023+.
[arXiv][Publisher Site].
- Semisupervised Regression in Latent Structure Networks on Unknown Manifolds
Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park, and Carey E. Priebe,
Applied Network Science, 2023+.
[arXiv][arXiv][Publisher Site].
- Spectral Graph Clustering via the Expectation-Solution Algorithm
Zachary Pisano, Joshua Agterberg, Carey Priebe, Daniel Naiman,
Electronic Journal of Statistics, 2022.
[arXiv][Publisher Site].
- Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms
Joshua Agterberg and Jeremias Sulam,
AISTATS, 2022.
[arXiv][Publisher Site].
- Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
Joshua Agterberg, Zachary Lubberts, and Carey Priebe,
IEEE Transactions on Information Theory, 2022+.
A recording of a talk covering this paper is available here.
[arXiv][Publisher Site].
- Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation: Applications in Connectomics
Jaewon Chung, Bijan Varjavand, Jesús Arroyo, Anton Alyakin, Joshua Agterberg, Minh Tang, Joshua Vogelstein, and Carey Priebe,
Stat, 2021.
[arXiv][Publisher Site].
- Vertex Nomination, Consistent Estimation, and Adversarial Modification
Joshua Agterberg, Youngser Park, Jonathan Larson, Chris White, Carey Priebe, and Vince Lyzinski,
Electronic Journal of Statistics, 2020.
[arXiv][Publisher Site].
- Social Determinant-Based Profiles of US Adults with the Highest and Lowest Health Expenditures Using Clusters
Fanghao Zhong, Margie Rosenberg, Joshua Agterberg, and Richard Crabb,
North American Actuarial Journal, 2020.
[Publisher Site]
- Cluster Analysis Application to Identify Groups of Individuals with High Health Expenditures
Joshua Agterberg, Fanghao Zhong, Richard Crabb, and Margie Rosenberg,
Health Services and Outcomes Research Methodology, 2020.
[Publisher Site].
Honors and Awards
Reading Groups
Current and Previous Collaborators