2 citations · 3 across the 3 of their papers we have counts for
7 papers
Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
Si Cheng, Magali N. Blanco, Lianne Sheppard +2
Exposure assessment is fundamental to air pollution cohort studies. The objective is to predict air pollution exposures for study subjects at locations without data in order to opt…
Regularised Spectral Estimation for High-Dimensional Point Processes
Carla Pinkney, Carolina Euan, Alex Gibberd +1
Advances in modern technology have enabled the simultaneous recording of neural spiking activity, which statistically can be represented by a multivariate point process. We charact…
Learning Directed Acyclic Graphs from Partial Orderings
Ali Shojaie, Wenyu Chen
Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistica…
Inference for Heterogeneous Graphical Models using Doubly High-Dimensional Linear-Mixed Models
Kun Yue, Eardi Lila, Ali Shojaie
Motivated by the problem of inferring the graph structure of functional connectivity networks from multi-level functional magnetic resonance imaging data, we develop a valid infere…
Network Differential Connectivity Analysis
Sen Zhao, Stephen Ottinger, Suzanne Peck +2
Identifying differences in networks has become a canonical problem in many biological applications. Here, we focus on testing whether two Gaussian graphical models are the same. Ex…
Network Reconstruction From High Dimensional Ordinary Differential Equations
Shizhe Chen, Ali Shojaie, Daniela M. Witten
We consider the task of learning a dynamical system from high-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression d…