1. Brown, P.J., Vannucci, M. and Fearn, T. (1998). Multivariate Bayesian Variable Selection and Prediction. Journal of the Royal Statistical Society, Series B, 60(3), 627-641. This paper extends Bayesian variable selection methods that use mixture prior distributions with a spike at zero to multivariate regression settings.
2. Brown, P.J., Fearn, T. and Vannucci, M. (2001). Bayesian Wavelet Regression on Curves with Application to a Spectroscopic Calibration Problem. Journal of the American Statistical Association, 96, 398-408. This paper proposes a Bayesian wavelet-based model of functional data and it is one of the very first contributions to the use of wavelet methods for dimension reduction when multiple curves are under study.
3. Tadesse, M.G., Sha, N. and Vannucci, M. (2005). Bayesian Variable Selection in Clustering High-Dimensional Data. Journal of the American Statistical Association, 100, 602-617. This paper proposes an innovative way to perform Bayesian variable selection in model-based sample clustering via mixture models.
4. Savitsky, T., Vannucci, M. and Sha, N. (2011). Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies. Statistical Science, 26(1), 130-149. This paper considers variable selection in a generalized linear framework and introduces a stochastic search MCMC algorithm that jointly updates parameters and selection indicators. This is key to design efficient sampling algorithms that avoid reversible jump with discrete spike-and-slab priors.
5. Stingo, F.C., Chen Y.A., Tadesse, M.G. and Vannucci, M. (2011). Incorporating Biological Information into Linear Models: A Bayesian Approach to the Selection of Pathways and Genes. Annals of Applied Statistics, 5(3), 1978-2002. This paper develops a Bayesian variable selection regression framework that incorporates prior information on latent structures in the variables. The model is specifically designed to account for the relationships between genes and their membership to biochemical pathways via Markov random field priors.
6. Peterson, C.B., Stingo, F.C. and Vannucci, M. (2015). Bayesian Inference of Multiple Gaussian Graphical Models. Journal of the American Statistical Association, 110, 159-174. This paper is one of the first contributions in the literature to Bayesian estimation of multiple undirected graphs in situations where some of the graphs may be unrelated, while others share common features.
7. Warnick, R., Guindani, M., Erhardt, E., Allen, E., Calhoun, V. and Vannucci, M. (2018). A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data. Journal of the American Statistical Association, 113, 134-151. This paper proposes the first principled Bayesian approach to the estimation of dynamic, e.g, time-varying, brain connectivity networks, a topic that has attracted considerable attention in the neuroimaging literature.
8. Chiang, S., Vannucci, M., Goldenholz, D., Moss, R. and Stern, J.M. (2018). Epilepsy as a Dynamic Disease: A Bayesian Model for Differentiating Seizure Risk from Natural Variability. Epilepsia Open, 3(2):236–246. This paper is the first to formalize the notion of unknown seizure risk “states”, showing that seizure risk of epileptic patients can be estimated as a latent quantity based on their seizure counts. Winner of the Clinical Article Award, Epilepsia Open Editors’ choice.
9. van de Schoot, R., Depaoli, S., King, R., Kramer, B., Martens, K.,Tadesse, M.G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J. and Yau, C. (2021). Bayesian Statistics and Modelling. Nature Reviews Methods Primers,1, article 1 (invited contribution). This is a comprehensive Primer on Bayesian statistics and modeling, covering models, priors, posterior algorithms, predictive checks, variable selection, and applications from psychology to genetics. It is widely cited across many scientific fields (900+ citations in Google Scholar within the first 4 years from publication).
10. Wang, E.T.,Vannucci, M., Haneef, Z., Moss, R., Rao, V.R. and Chiang, S. (2022). A Bayesian Switching Linear Dynamical System for Estimating Seizure Chronotypes. Proceedings of the National Academy of Sciences, 119(46), e2200822119.6. This paper develops a dynamical system framework specifically designed for the analysis of seizure counts, which helps better understanding and predicting the timing of seizure cycles.
