22 citations · 45 across the 12 of their papers we have counts for
6 papers · 1 filter
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…