5 papers
Bayesian Scalar-on-Tensor Quantile Regression for Longitudinal Data on Alzheimer's Disease
Rongke Lyu, Marina Vannucci, Suprateek Kundu
As a general and robust alternative to traditional mean regression models, quantile regression avoids the assumption of normally distributed errors, making it a versatile choice wh…
Bayesian Covariate-Dependent Graph Learning with a Dual Group Spike-and-Slab Prior
Zijian Zeng, Meng Li, Marina Vannucci
Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges…
Bayesian network-guided sparse regression with flexible varying effects
Yangfan Ren, Christine B. Peterson, Marina Vannucci
In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allo…
Discrete Autoregressive Switching Processes with Cumulative Shrinkage Priors for Graphical Modeling of Time Series Data
Beniamino Hadj-Amar, Aaron M. Bornstein, Michele Guindani +1
We propose a flexible Bayesian approach for sparse Gaussian graphical modeling of multivariate time series. We account for temporal correlation in the data by assuming that observa…
Semiparametric Latent ANOVA Model for Event-Related Potentials
Cheng-Han Yu, Meng Li, Marina Vannucci
Event-related potentials (ERPs) extracted from electroencephalography (EEG) data in response to stimuli are widely used in psychological and neuroscience experiments. A major goal…