137 citations · 277 across the 8 of their papers we have counts for
10 papers
Flexible parametrization of graph-theoretical features from individual-specific networks for prediction
Mariella Gregorich, Sean L. Simpson, Georg Heinze
Statistical techniques are needed to analyse data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which…
A Note on Implementing a Special Case of the LEAR Covariance Model in Standard Software
Sean L. Simpson, Min Zhu, Keith E. Muller
Repeated measures analyses require proper choice of the correlation model to ensure accurate inference and optimal efficiency. The linear exponent autoregressive (LEAR) correlation…
Disentangling Brain Graphs: A Note on the Conflation of Network and Connectivity Analyses
Sean L. Simpson, Paul J. Laurienti
Understanding the human brain remains the Holy Grail in biomedical science, and arguably in all of the sciences. Our brains represent the most complex systems in the world (and som…
A Two-Part Mixed-Effects Modeling Framework For Analyzing Whole-Brain Network Data
Sean L. Simpson, Paul J. Laurienti
Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses a…
Analyzing complex functional brain networks: fusing statistics and network science to understand the brain
Sean L. Simpson, F. DuBois Bowman, Paul J. Laurienti
Complex functional brain network analyses have exploded over the last eight years, gaining traction due to their profound clinical implications. The application of network science…
An Adjusted Likelihood Ratio Test for Separability in Unbalanced Multivariate Repeated Measures Data
Sean L. Simpson
We propose an adjusted likelihood ratio test of two-factor separability (Kronecker product structure) for unbalanced multivariate repeated measures data. Here we address the partic…