4 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…
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…
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…