7 papers
A Bayesian Framework for Quantifying Association Between Functional and Structural Data in Neuroimaging
Sakul Mahat, Sharmistha Guha, Jessica Bernard
Structural and functional neuroimaging modalities provide complementary windows into brain organization: structural imaging characterizes neural tissue anatomy and microstructure,…
Integrative Predictor-Dependent Learning of Network Data and Spatially Correlated Nodal Attributes for Multimodal Brain Imaging in Aging
Jose Rodriguez-Acosta, Sharmistha Guha, Jessica Bernard +2
This article introduces a predictor-dependent joint modeling framework for network data obtained from multiple subjects over a shared set of nodes with spatial co-ordinates and spa…
Integrative Learning of Dynamically Evolving Multiplex Graphs and Nodal Attributes Using Neural Network Gaussian Processes with an Application to Dynamic Terrorism Graphs
Jose Rodriguez-Acosta, Sharmistha Guha, Lekha Patel +1
Exploring the dynamic co-evolution of multiplex graphs and nodal attributes is a compelling question in criminal and terrorism networks. This article is motivated by the study of d…
Differentially Private Estimation of Weighted Average Treatment Effects for Binary Outcomes
Sharmistha Guha, Jerome P. Reiter
In the social and health sciences, researchers often make causal inferences using sensitive variables. These researchers, as well as the data holders themselves, may be ethically a…
High Dimensional Bayesian Network Classification with Network Global-Local Shrinkage Priors
Sharmistha Guha, Abel Rodriguez
This article proposes a novel Bayesian classification framework for networks with labeled nodes. While literature on statistical modeling of network data typically involves analysi…
Bayesian Causal Inference with Bipartite Record Linkage
Sharmistha Guha, Jerome P. Reiter, Andrea Mercatanti
In many scenarios, the observational data needed for causal inferences are spread over two data files. In particular, we consider scenarios where one file includes covariates and t…