MARINA VANNUCCI - Department of Statistics - Rice University

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Marina Vannucci - July 2014 814. Park, C.G., Vannucci, M. and Hart, J.D. (2005). Bayesian Methods for wavelet series in single-indexmodels. Journal of Computational and Graphical Statistics, 14(4), 770–794.15. Kim, S., Tadesse, M.G. and Vannucci, M. (2006). Variable selection in clustering via Dirichlet processmixture models. Biometrika, 93(4), 877–893. Winner of the ASA-SBSS Student Paper competition.16. Ko, K. and Vannucci, M. (2006). Bayesian wavelet analysis of autoregressive fractionally integrated movingaverageprocesses. Journal of Statistical Planning and Inference, 136(10), 3415–3434.17. Lee, S., Lim, J., Vannucci, M., Petkova, E., Preter, M. and Klein, D.F. (2008). Order-preservingdimension reduction test for the dominance of two mean curves with application to tidal volume curves.Biometrics, 64(3), 931–939. PMID:18177460.18. Dahl, D.B., Mo, Q. and Vannucci, M. (2008). Simultaneous inference for multiple testing and clusteringvia a Dirichlet process mixture model. Statistical Modelling: An International Journal, 8(1), 23–39.19. Swartz, M.D., Mo, Q., Murphy, M.E., Turner, N., Lupton, J., Hong, M.Y. and Vannucci, M.(2008). Bayesian variable selection in clustering high dimensional data with substructure. Journal of Agricultural,Biological and Environmental Statistics, 13(4), 407–423.20. Lennox, K.P., Dahl, D.B., Vannucci, M. and Tsai, J.W. (2009). Density estimation for protein conformationangles using a von Mises distribution and Bayesian nonparametrics, Journal of the American StatisticalAssociation, 104, 586–596. Correction in 104, 1728. Winner of the ASA-SBSS Student Paper competition.PMID:20221312. PMCID:PMC2835366.21. Ko, K., Qu, L. and Vannucci, M. (2009). Wavelet-based Bayesian estimation of partially linear regressionmodels with long memory errors. Statistica Sinica, 19(4), 1463–1478. PMID:23946613. PMCID:PMC374097822. Kim, S., Dahl, D.B. and Vannucci, M. (2009). Spiked Dirichlet process prior for Bayesian multiplehypothesis testing in random effects models, Bayesian Analysis, 4(4), 707–732. PMID:23950766. PM-CID:PMC3741668.23. Zhu, H., Vannucci, M. and Cox, D.D. (2010). A Bayesian hierarchical model for classification with selectionof functional predictors. Biometrics, 66(2), 463–473. Winner of the ASA-SBSS Student Papercompetition. PMID:19508236. PMCID:PMC3042776.24. Lennox, K.P., Dahl, D.B., Vannucci, M., Day, R. and Tsai, J.W. (2010). A Dirichlet process mixtureof hidden Markov models for protein structure prediction. Annals of Applied Statistics, 4(2), 916–942.PMID:21031154. PMCID:PMC2964143.25. Stingo, F.C., Chen, Y.A., Vannucci, M., Barrier, M. and Mirkes, P.E. (2010). A Bayesian graphicalmodeling approach to microRNA regulatory network inference. Annals of Applied Statistics, 4(4), 2024–2048.PMID:23946863. PMCID:PMC3740979. Code available.26. Savitsky, T. and Vannucci, M.(2010). Spiked Dirichlet process priors for Gaussian process models. Journalof Probability and Statistics, 2010, Article ID 201489, 14 pages. PMID:23950763. PMCID:PMC3742051.27. Stingo, F.C., Chen Y.A., Tadesse, M.G. and Vannucci, M. (2011). Incorporating Biological Informationinto Linear Models: A Bayesian Approach to the Selection of Pathways and Genes. Annals of Applied Statistics,5(3), 1978–2002. PMID:23667412. PMCID:PMC3650864. Code available.28. Savitsky, T., Vannucci, M. and Sha, N. (2011). Variable Selection for Nonparametric Gaussian ProcessPriors: Models and Computational Strategies. Statistical Science, 26(1), 130–149. PMID:23950763. PM-CID:PMC3742051. Code available.
Marina Vannucci - July 2014 929. Kwon, D.W., Landi, M.T., Vannucci, M., Issaq, H.J., Prieto, D. and Pfeiffer, R.M. (2011). Anefficient stochastic search for Bayesian variable selection with high-dimensional correlated predictors. ComputationalStatistics and Data Analysis, 55(10), 2807–2818. PMID:21686315. PMCID:PMC311347930. Stingo, F.C., Vannucci, M. and Downey, G. (2012). Bayesian Wavelet-based Curve Classification viaDiscriminant Analysis with Markov Random Tree Priors. Statistica Sinica, 22, 465–488.31. Lee, S.H., Lim, J., Li, E., Vannucci, M. and Petkova, E. (2012). Order test for high-dimensional twosample means. Journal of Statistical Planning and Inference, 142, 2719–2725.32. Stingo, F.C., Guindani, M., Vannucci, M. and Calhoun, V. (2013). An Integrative Bayesian ModelingApproach to Imaging Genetics. Journal of the American Statistical Association, 108, 876–891. Code available.33. Jeong, J., Vannucci, M. and Ko, K. (2013). A Wavelet-based Bayesian Approach to Regression Modelswith Long Memory Errors and its Application to fMRI Data, Biometrics, 69(1), 184–196.34. Allen, G.I., Peterson, C.B., Vannucci, M. and Maletic-Savatic, M. (2013). Regularized Partial LeastSquares with an Application to NMR Spectroscopy. Statistical Analysis and Data Mining, 6(4), 302–314.35. Peterson, C.B., Vannucci, M., Karakas, C., Choi, W., Ma, L. and Maletic-Savatic, M. (2013).Inferring Metabolic Networks Using the Bayesian Adaptive Graphical Lasso with Informative Priors, Statisticsand Its Interface, 6, 547–558.36. Brownlees, C. and Vannucci, M. (2013). A Bayesian Approach for Capturing Daily Heterogeneity inIntra-Daily Durations Time Series. Studies in Nonlinear Dynamics and Econometrics, 17(1), 21–46.37. Cassese, A., Guindani, M., Tadesse, M., Falciani, F. and Vannucci, M. (2014). A HierarchicalBayesian Model for Inference on Copy Number Variants and their Association to Gene Expression. Annals ofApplied Statistics, 8(1), 148–175. Code available.38. Peterson, C.B., Stingo, F.C. and Vannucci, M. (2014). Bayesian Inference of Multiple Gaussian GraphicalModels. Journal of the American Statistical Association, accepted. Winner of the ASA-SBSS StudentPaper competition. Code available.39. Stingo, F.C., Swartz, M.D. and Vannucci, M. (2014). A Bayesian Approach for the Identification ofGenes and Gene-level SNP Aggregates in a Genetic Analysis of Cancer Data. Statistics and Its Interface,accepted.40. Villagran, A., Huerta, G., Vannucci, M., Jackson, C.S. and Nosedal, A. (2014). Non-ParametricSampling Approximation via Voronoi Tessellations. Communications in Statistics - Simulation and Computation,accepted.41. Waters, A.E., Fronczyk, K., Guindani, M., Baraniuk, R.G. and Vannucci, M. (2014). A BayesianNonparametric Approach for the Analysis of Multiple Categorical Item Responses. Journal of StatisticalPlanning and Inference, invited revision.42. Zhang, L., Guindani, M. and Vannucci, M. (2014). Bayesian Models for fMRI Data Analysis. WIREsComputational Statistics, Submitted (invited contribution).Methods & Applications: Neuroscience & Neuroimaging43. Koshelev, M., Lohrenz, T., Vannucci, M. and Montague, P.R. (2010). Biosensor Approach to PsychopathologyClassification. PLoS Computational Biology, 6(10), e1000966. PMID:20975934. PMCID:PMC2958801.
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