9 citations · 10 across the 5 of their papers we have counts for
7 papers · 1 filter
Bayesian estimation of clustered dependence structures in functional neuroconnectivity
Hyoshin Kim, Sujit K. Ghosh, Adriana Di Martino +1
Motivated by the need to model the dependence between regions of interest in functional neuroconnectivity for efficient inference, we propose a new sampling-based Bayesian clusteri…
Distributed model building and recursive integration for big spatial data modeling
Emily C. Hector, Brian J. Reich, Ani Eloyan
Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gauss…
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS
Weiqiong Huang, Emily C. Hector, Joshua Cape +1
The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of…
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)
Jimmy Hickey, Jonathan P. Williams, Emily C. Hector
Transfer learning uses a data model, trained to make predictions or inferences on data from one population, to make reliable predictions or inferences on data from another populati…
Fused mean structure learning in data integration with dependence
Emily C. Hector
Motivated by image-on-scalar regression with data aggregated across multiple sites, we consider a setting in which multiple independent studies each collect multiple dependent vect…
Distributed Inference for Spatial Extremes Modeling in High Dimensions
Emily C. Hector, Brian J. Reich
Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using max stable processes (MSPs). MSPs are computationally prohibitiv…