Group penalized generalized estimating equation for correlated event-related potentials and biomarker selection

Variable selection for recurrent event data via nonconcave penalized estimating function.

Variable selection is an important issue in all regression analysis and in this paper, we discuss this in the context of regression analysis of recurrent event data. Recurrent event data often occur in long-term studies in which individuals may experience the events of interest more than once and their analysis has recently attracted a great deal of attention (Andersen et al., Statistical model...

Probabilistic delay differential equation modeling of event-related potentials

"Dynamic causal models" (DCMs) are a promising approach in the analysis of functional neuroimaging data due to their biophysical interpretability and their consolidation of functional-segregative and functional-integrative propositions. In this theoretical note we are concerned with the DCM framework for electroencephalographically recorded event-related potentials (ERP-DCM). Intuitively, ERP-D...

Penalized generalized estimating equations for high-dimensional longitudinal data analysis.

We consider the penalized generalized estimating equations (GEEs) for analyzing longitudinal data with high-dimensional covariates, which often arise in microarray experiments and large-scale health studies. Existing high-dimensional regression procedures often assume independent data and rely on the likelihood function. Construction of a feasible joint likelihood function for high-dimensional ...

1 Event Related Potentials

Event - Related Potentials

11 Electroencephalography is one of the most popular psychophysiological methods in clinical and 12 preclinical research and provides a recording that reflects the global electrical activity of the 13 brain – the EEG. A limitation of the EEG is that it represents the summation of all the electrical 14 activity at a given moment in time, making it difficult to isolate the activity associated wit...