[1] Mixed Membership Estimation in Two-Path Partial Correlation Network
[Job Market Paper] [Paper]
Abstract: We propose a methodology for estimating mixed-membership structure in large panels of time series.
Unlike pure-membership models, in which each unit belongs exclusively to one group, mixed-membership models allow
each unit to have partial affiliations with multiple latent groups. We develop a two-path partial-correlation network
model in which the sparsity pattern and interaction intensity are governed separately by mixed membership and
node-specific sociability. These latent structures are integrated into a unified probabilistic system through a
Bayesian network and enter the panel model through the innovation process.
We then propose a spectral algorithm to estimate the latent mixed-membership structure.
[2] ML-Assisted Empirical Bayes Estimation for Group Regression with Network Data
[Paper]
Abstract: We introduce a network heterogeneity exposure model
that encompasses several network-based panel models as special cases. The framework gives rise to a novel
peer-effects specification in which units are affected by their exposure to neighbors’ unit-specific heterogeneity,
rather than by neighbors’ outcomes directly. While the OLS estimator exists under this model, it can perform poorly
when the underlying network is sparse. We therefore develop a machine-learning-assisted empirical Bayes estimation
framework that combines a structural causal model with a Bayesian belief network. This framework yields a
class of empirical Bayes estimators designed specifically for the proposed network heterogeneity exposure model.
[3] The Horizon Structure of Contagion: High-Dimensional Measurement and Inference
[Available upon request]
with Eugene Dettaa, Abderrahim Taamouti and Endong Wang
Abstract: Write a short abstract here.
[4] Mixed Membership Estimation in Bayesian Network Autoregression
[Paper]
with Endong Wang
Abstract: We propose a network autoregression to learn mixed membership in large panel of time series.
The data generating process, coefficient matrix and Mixed membership structure are integrated into one
unified system using joint distribution specified by probabilistic graph.
We also propose a set of algorithm to learn latent mixed membership structure with the model.
[5] Overlapping Community Detection in Mixed Membership Vector Autoregression
[Paper]
Abstract: We propose a mixed membership stochastic block vector autoregression which allows for multiple memberships in large panel of time series.
A set of algorithm is proposed to learn the multiple membership structure with the model. We prove the consistency of proposed algorithm.
[1] Bi-group Detection in Large Matrix-Variate Factor Model
with Endong Wang
Abstract: Write a short abstract here.