5 papers
How useful is a wrong model? Information-sharing for inference under mean misspecification in linear models
Emily C. Hector
As almost all models are wrong, a mean model's usefulness is often accepted as sufficient justification for its use. In practice, however, standard statistical theory breaks when t…
Heterogeneity-adaptive shrinkage estimation and inference for linear panel models
Elizabeth M. Davis, Emily C. Hector
Recent shrinkage estimators for linear panel models have focused on increasing flexibility beyond empirical Bayes estimators to deliver mean squared error optimality, but these est…
Estimating Covariate Effects on Functional Connectivity using Voxel-Level fMRI Data
Wei Zhao, Brian J. Reich, Emily C. Hector
Functional connectivity (FC) analysis of resting-state fMRI data provides a framework for characterizing brain networks and their association with participant-level covariates. Due…
Divide-and-conquer with finite sample sizes: valid and efficient possibilistic inference
Emily C. Hector, Leonardo Cella, Ryan Martin
Divide-and-conquer methods use large-sample approximations to provide frequentist guarantees when each block of data is both small enough to facilitate efficient computation and la…
A new block covariance regression model and inferential framework for massively large neuroimaging data
Hyoshin Kim, Sujit K. Ghosh, Emily C. Hector
Some evidence suggests that people with autism spectrum disorder exhibit patterns of brain functional dysconnectivity relative to their typically developing peers, but specific fin…