22 citations · 45 across the 12 of their papers we have counts for
13 papers
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…
Statistical Inference for Streamed Longitudinal Data
Lan Luo, Jingshen Wang, Emily C. Hector
Modern longitudinal data, for example from wearable devices, measures biological signals on a fixed set of participants at a diverging number of time points. Traditional statistica…
Functional Regression with Intensively Measured Longitudinal Outcomes: A New Lens through Data Partitioning
Cole Manschot, Emily C. Hector
Estimation and inference with modern longitudinal data from wearable devices, which consist of biological signals at high-frequency time points, is burdened by massive computationa…
Turning the information-sharing dial: efficient inference from different data sources
Emily C. Hector, Ryan Martin
A fundamental aspect of statistics is the integration of data from different sources. Classically, Fisher and others were focused on how to integrate homogeneous (or only mildly he…
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…
Parallel-and-stream accelerator for computationally fast supervised learning
Emily C. Hector, Lan Luo, Peter X. -K. Song
Two dominant distributed computing strategies have emerged to overcome the computational bottleneck of supervised learning with big data: parallel data processing in the MapReduce…
Joint integrative analysis of multiple data sources with correlated vector outcomes
Emily C. Hector, Peter X. -K. Song
We propose a distributed quadratic inference function framework to jointly estimate regression parameters from multiple potentially heterogeneous data sources with correlated vecto…
Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes
Emily C. Hector, Peter X. -K. Song
This paper presents a unified framework for supervised learning and inference procedures using the divide-and-conquer approach for high-dimensional correlated outcomes. We propose…
A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis
Emily C. Hector, Peter X. -K. Song
This paper is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multi-level nested correlations.…