In progress
[182] Focardi-Olmi, L., Gottard, A., Guindani, M. and Vannucci, M. (2024). Bayesian Controlled FDR Variable Selection via Knockoffs. Submitted.
[181] Florez, M., Gottard, A., McAdams, C., Guindani, M. and Vannucci, M. (2024). Bayesian Mixed Graphical Models. Bayesian Analysis, invited revision.
[180] Ricci, F.Z., Sudderth, E.B., Lee, J., Peters, M.A.K., Vannucci, M. and Guindani, M. (2024). Bayesian Temporal Biclustering with Applications to Multi-Subject Neuroscience Studies. Under revision.
[179] Giampino, A., Guindani, M., Nipoti, B. and Vannucci, M. (2024). Local Level Dynamic Random Partition Models for Changepoint Detection. Under revision.
[178] Hadj-Amar, B., Bornstein, A.M., Guindani, M. and Vannucci, M. (2024). Sparse Gaussian Graphical Modeling of High-Dimensional Time Series with Discrete Autoregressive Processes. Journal of Computational & Graphical Statistics, under revision.
[177] Zeng, Z., Li, M. and Vannucci, M. (2024). Bayesian Covariate-Dependent Graph Learning with a Dual Group Spike-and-Slab Prior. Biometrics, under revision.
[176] Hadj-Amar, B., Krishnan, V. and Vannucci, M. (2024). Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms. Data Science in Science, invited revision.
2025-
[175] Liu, C., Kowal, D.R., Doss-Gollin, J. and Vannucci, M. (2025). Bayesian Functional Graphical Models with Change-Point Detection. Computational Statistics & Data Analysis, 206, 108122.
[174] Florez, M., Guindani, M. and Vannucci, M. (2025). Bayesian Bivariate Conway-Maxwell-Poisson Regression Model for Correlated Count Data in Sports. Journal of Quantitative Analysis in Sports, in press.
[173] Batten, S.R., Hartle, A., Barbosa, L.S., Hadj-Amar, B., Bang, D., Melville, N., Twomy, T., White, J.P., Torres, A., Celaya, X., McClure, S.M., Brewer, G.A., Lohrenz, T., Kishida, K.T., Bina, R.W., Witcher, M.R., Vannucci, M., King-Casas, B., Chiu, P., Montague, P.R. and Howe, W.M. (2025). Emotional words evoke region and valence-specific patterns of concurrent neuromodulator release in human thalamus and cortex. Cell Reports, 44(1), 115162.
2023-2024
[172] Li, M., Liu, Z., Yu, C.-H. and Vannucci, M. (2024). Semiparametric Bayesian inference for local extrema of functions in the presence of noise. Journal of the American Statistical Association, 119, 3127-3140.
[171] Hadj-Amar, B., Jewson, J. and Vannucci, M. (2024). Bayesian Sparse Vector Autoregressive Switching Models with Application to Human Gesture Phase Segmentation. Annals of Applied Statistics, 18(3), 2511-2531.
[170] Song, Z., Shen, W., Vannucci, M., Baldizon, A., Cinciripini, P.M., Versace, F. and Guindani, M. (2024). Clustering Computer Mouse Tracking Data with Informed Hierarchical Shrinkage Partition Priors. Biometrics, 80(4), ujae124.
[169] Ren, Y., Peterson, C.B. and Vannucci, M. (2024). Bayesian network-guided sparse regression with flexible varying effects. Biometrics, 80(4), ujae111.
[168] Liang, M., Koslovsky, M.D., Hebert, E.T., Businelle, M.S. and Vannucci, M. (2024) Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors. Bayesian Analysis, 19(4), 1067-1095.
[167] Zeng, Z., Li, M. and Vannucci, M. (2024). Bayesian Image-on-Scalar Regression with a Spatial Global-Local Spike-and-Slab Prior. Bayesian Analysis, 19(1), 235-260.
[166] Liang, M., Koslovsky, M.D., Hebert, E.T., Kendzor, D.E. and Vannucci, M. (2024). A Bayesian Nonparametric Approach for Clustering Functional Trajectories over Time. Statistics and Computing, 34, 215.
[165] Yu, C.-H., Li, M. and Vannucci, M. (2024). Semiparametric Latent ANOVA Model for Event-Related Potentials. Data Science in Science, 3(1), article 2294204.
[164] Pluta, D., Hadj-Amar, B., Li, M., Zhao, Y., Versace, F. and Vannucci, M. (2024). Improved Data Quality and Statistical Power of Trial-Level Event-Related Potentials with Bayesian Random-Shift Gaussian Processes. Scientific Reports, 14, article 8856.
[163] Ren, Y., Osborne, N., Peterson, C.B., DeMaster, D.M., Ewing-Cobbs, L. and Vannucci, M. (2024). Bayesian Varying-Effects Vector Autoregressive Models for Inference of Brain Connectivity Networks and Covariate Effects in Pediatric Traumatic Brain Injury. Human Brain Mapping, 45(10), e26763.
[162] Lyu, R., Vannucci, M. and Kundu, S. et al. (2024). Bayesian Tensor Modeling for Image-based Classification of Alzheimer’s Disease. Neuroinformatics, 22, 437-455.
[161] Yi, L., Pluta, D. Brodrick, B.B., Palka, J.M., McCoy, J., Lohrenz, T., Gu, X., Vannucci, M., Montague, P.R. and McAdams, C.J. (2024). Diminished adaptation, satisfaction and neural responses to advantageous social signals in anorexia nervosa and bulimia nervosa. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 9(3), 305-313.
[160] Lee, J., Hussain, S., Warnick, R., Vannucci, M., Menchaca, I., Seitz, A.R., Hu, X., Peters, M.A.K. and Guindani, M. (2024). A Predictor-Informed Multi-Subject Bayesian Approach for Dynamic Functional Connectivity. PLoS ONE, 19(5): e0298651.
[159] Lyu, R., Guindani, M. and Vannucci, M. (2024). Spatial Modeling of Imaging and Electrophysiological Data. In Statistical Methods in Epilepsy. Sharon Chiang, Vikram R. Rao and Marina Vannucci (Eds). Chapman & Hall/CRC, 227-247.
[158] Chiang, S., Zito, J., Rao, V.R. and Vannucci, M. (2024). Time-Series Analysis. In Statistical Methods in Epilepsy. Sharon Chiang, Vikram R. Rao and Marina Vannucci (Eds). Chapman & Hall/CRC, 166-200.
[157] Chen, Z., Luo, H., Kundu, S. and Vannucci, M. (2024). Tensor Decision Trees For High-Resolution Imaging Data. 2024 IEEE International Conference on Big Data (BigData). Pages: 8644-8646
[156] Wang, E.T., Chiang, S., Haneef, Z., Rao, V.R., Moss, R. and Vannucci, M. (2023). Bayesian Non-Homogeneous Hidden Markov Model with Variable Selection for Investigating Drivers of Seizure Risk Cycling. Annals of Applied Statistics, 17(1), 333-356.
[155] Yu, C.-H., Li, M., Noe, C., Fischer-Baum. S. and Vannucci, M. (2023). Bayesian Inference for Stationary Points in Gaussian Process Regression Models for Event-Related Potentials Analysis. Biometrics, 79(2), 629-641.
[154] Fu, J., Koslovsky, M.D. and Vannucci, M. (2023). A Bayesian Joint Model for Mediation Effect Selection in Compositional Microbiome Data. Statistics in Medicine, 42(17), 2999-3015.
[153] Liang, M., Koslovsky, M.D., Hebert, E., Businelle, M., Kendzor, D. and Vannucci, M. (2023). Bayesian Continuous-Time Hidden Markov Models with Covariate Selection for Intensive Longitudinal Data with Measurement Error. Psychological Methods, 28(4), 880–894.
[152] Bang, D., Luo, Y., Barbosa, L., Batten, S., Hadj-Amar, B., Twomey, T., Melville, N., White, J., Torres, A., Celaya, X., Ramaiah, P., McClure, S.M., Brewer, G.A., Bina, R.W., Lohrenz, T., King-Casas, B., Chiu, P., Vannucci, M., Kishida, K., Witcher, M. and Montague, P.R. (2023). Noradrenaline tracks emotional modulation of attention in human amygdala. Current Biology, 33(22), 5003-5010. [Commentary: Kim, A. J. (2023). Noradrenaline: Can we now directly measure in humans? Current Biology, 33(24), R1294-R1296]
[151] Shoemaker, K., Ger, R., Court, L., Aerts, H., Vannucci, M. and Peterson, C.B. (2023). Bayesian feature selection for radiomics using reliability metrics. Frontiers in Genetics, 14, doi: 10.3389/fgene.2023.1112914.
2021-2022
[150] 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.
[149] Osborne, N., Peterson, C.B. and Vannucci, M. (2022). Latent Network Estimation and Variable Selection for Compositional Data via Variational EM. Journal of Computational and Graphical Statistics, 31(1), 163-175.
[148] Liu, C., Kowal, D.R. and Vannucci, M. (2022). Dynamic and Robust Bayesian Graphical Models. Statistics and Computing, 32, 105.
[147] Ni, Y., Baladandayuthapani, V., Vannucci, M. and Stingo, F.C. (2022). Bayesian Graphical Models for Modern Biological Applications (with discussion and rejoinder). Statistical Methods and Applications, 31, 197-225.
[146] Wang, E.T., Chiang, S., Cleboski, S., Rao, V.R., Vannucci, M. and Haneef, Z. (2022). Seizure Count Forecasting to Aid Diagnostic Testing in Epilepsy. Epilepsia, 63(12), 3156-3167.
[145] Vaughn, K.A., DeMaster, D., Kook, J.H., Vannucci, M. and Ewing-Cobbs, L. (2022). Effective Connectivity in the Default Mode Network after Paediatric Traumatic Brain Injury. European Journal of Neuroscience, 55(1), 318-336.
[144] Godlewska, B., DeMaster, D., Liang, M., Vannucci, M., Bockmann, T., Bo, C. and Selvaraj, S. (2022). Effective Connectivity between Resting-State Networks in Depression. Journal of Affective Disorders, 307, 79-86.
[143] 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).
[142] Denti, F., Guindani, M., Leisen, F., Lijoi, A., Wadsworth, D. and Vannucci, M. (2021). Two-group Poisson-Dirichlet Mixtures for Multiple Testing. Biometrics, 77(2), 634-648.
[141] Chiang, S., Khambhati, A.N., Wang, E.T., Vannucci, M., Chang, E.F. and Rao, V.R. (2021). Evidence of State-Dependence in the Effectiveness of Responsive Neurostimulation for Seizure Modulation. Brain Stimulation, 14(2), 366-375.
[140] Kook, J.H., Vaughn, K., DeMaster, D., Ewing-Cobbs, L. and Vannucci, M. (2021). BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks. Neuroinformatics, 19, 39–56.
[139] Vannucci, M. (2021). Discrete Spike-and-Slab Priors: Models and Computational Aspects. In Handbook of Bayesian Variable Selection, Mahlet Tadesse and Marina Vannucci (Eds). Chapman & Hall/CRC, 3-24. [Note: the printed version of the chapter contains some incorrect references] [Slides from invited talk at ISBA 2022].
[138] Koslovsky, M.D. and Vannucci, M. (2021). Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data. In Statistical Analysis of Microbiome Data, Somnath Datta and Subharup Guha (Eds). Springer, Cham, 249-270.
2019-2020
[137] Argiento, R., Cremaschi, A. and Vannucci, M. (2020). Hierarchical Normalized Completely Random Measures to Cluster Grouped Data. Journal of the American Statistical Association, 115, 318-333.
[136] Koslovsky, M.D., Hebert, E.T., Businelle, M.S. and Vannucci, M. (2020). A Bayesian Time-Varying Effect Model for Behavioral mHealth Data. Annals of Applied Statistics, 14(4), 1878-1902.
[135] Koslovsky, M.D., Hoffman, K.L., Daniel, C.R. and Vannucci, M. (2020). A Bayesian Model of Microbiome Data for Simultaneous Identification of Covariate Associations and Prediction of Phenotypic Outcomes. Annals of Applied Statistics, 14(3), 1471-1492.
[134] Peterson, C.B., Osborne, N., Stingo, F., Bourgeat, P., Doecke, J.D. and Vannucci, M. (2020). Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer’s Disease. Biometrics, 76(4), 1120-1132.
[133] Shaddox, E., Peterson, C.B., Stingo, F.C., Hanania, N., Cruickshank-Quinn, C., Kechris, K., Bowler, R. and Vannucci, M. (2020). Bayesian Inference of Networks Across Multiple Sample Groups and Data Types. Biostatistics, 21(3), 561-576.
[132] Koslovsky, M.D. and Vannucci, M. (2020). MicroBVS: Dirichlet-Tree Multinomial Regression Models with Bayesian Variable Selection – an R package. BMC Bioinformatics, 21:301. DOI 10.1186/s12859-020-03640-0.
[131] Chiang, S., Goldenholz, D.M., Moss, R., Rao, V.R., Haneef, Z., Theodore, W.H., Kleen, J., Gavvala, J., Vannucci, M. and Stern, J.M. (2020). Prospective Validation Study of an Epilepsy Seizure Risk System for Outpatient Evaluation. Epilepsia, 61, 29–38.
[130] Miao, Y., Kowal, D.R., Panchal, N., Vila, J. and Vannucci, M. (2020). Nonlinear State-Space Modeling Approaches to Real-time Autonomous Geosteering. Journal of Petroleum Science and Engineering, 189, 107025.
[129] Miao, Y., Kook, J.H., Y. Lu, Guindani, M. and Vannucci, M. (2020). Scalable Bayesian Variable Selection Regression Models for Count Data. In Flexible Bayesian Regression Modelling, Yanan F., Smith M., Nott D. and Dortet-Bernadet J.-L.(Eds). Elsevier, 187-219.
[128]Li, Q., Cassese, A., Guindani, M. and Vannucci, M. (2019). Bayesian Negative Binomial Mixture Regression Models for the Analysis of Sequence Count and Methylation Data. Biometrics, 75(1), 183-192.
[127] Cremaschi, A., Argiento, R., Shoemaker, K., Peterson, C.B. and Vannucci M. (2019). Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling. Bayesian Analysis, 14(4), 1271-1301.
[126] Cassese, A., Zhu, W., Guindani, M. and Vannucci, M. (2019). A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection. Bayesian Analysis, 14(2), 553-572.
[125] Kook, J.H., Guindani, M., Zhang, L. and Vannucci, M. (2019). NPBayes-fMRI: Nonparametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data. Statistics in Biosciences, 11(1), 3-21.
[124] Miao, Y., Wu, M., Panchal, N., Kowal, D., Vannucci, M., Vila, J. and Liang, F. (2019). Stochastic Clustering and Pattern Matching for Real Time Geosteering. Geophysics, 84(5), ID13-ID24.
2017-2018
[123] 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.
[122] Mo, Q., Shen, R., Guo, C., Vannucci, M., Chan, K. and Hilsenbeck, S.G. (2018). A Fully Bayesian Latent Variable Model for Integrative Clustering Analysis of Multi-type Omics Data. Biostatistics, 19(1), 71-86. Software iClusterBayes at Bioconductor.
[121] Shaddox, E., Stingo, F., Peterson, C.B., Jacobson, S., Cruickshank-Quinn, C., Kechris, K., Bowler, R. and Vannucci, M. (2018). A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD. Statistics in Biosciences, 10(1), 59-85.
[120] Chiang, S., Vankov, E.R., Yeh, H.J., Guindani, M., Vannucci, M., Haneef, Z. and Stern, J.M. (2018). Temporal and Spectral Characteristics of Dynamic Functional Connectivity Between Resting-state Networks Reveal Information Beyond Static Connectivity. PLoS ONE, 13(1): e0190220.
[119] Fischer-Baum, S., Kook, J.-H., Lee, Y., Ramos-Nunez, A. and Vannucci, M. (2018). Individual Differences in the Neural and Cognitive Mechanism of Single Word Reading. Frontiers in Human Neuroscience, 12:271, doi:10.3389/fnhum.2018.00271.
[118] 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.
[117] Evans, L.C., Dayton, A., Yang, C., Liu, P. , Kurth, T., Ahn, K.W., Komas, S., Stingo, F.C., Laud, P.W., Vannucci, M., Liang, M. and Cowley, A.W. (2018). Transcriptomic Analysis Reveals Inflammatory and Metabolic Pathways which are Regulated by Renal Perfusion Pressure in Dahl-S Rats. Physiological Genomics, 50(6), 440-447.
[116] Guindani, M. and Vannucci, M. (2018). Challenges in the Analysis of Neuroscience Data. In Studies in Neural Data Science, Canale A., Durante D., Paci L., Scarpa B. (Eds). Springer Proceedings in Mathematics & Statistics, vol 257, 131-156.
[115] Chapple, A.G., Vannucci, M., Thall, P. and Lim, S.H. (2017). Bayesian Variable Selection for a Semi-Competing Risks Model with Multiple Components. Computational Statistics and Data Analysis, 112, 170-185.
[114] Li, Q., Guindani, M., Reich, B.J., Bondell, H.D. and Vannucci, M. (2017). A Bayesian Mixture Model for Clustering and Selection of Feature Occurrence Rates under Mean Constraints. Statistical Analysis and Data Mining, 10(6), 393-409.
[113] Chiang, S., Guindani, M., Yeh, H-J., Dewar, S., Haneef, Z., Stern, J.M. and Vannucci, M. (2017). A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome After Anterior Temporal Lobe Resection. Frontiers in Neuroscience, 11:669, doi: 10.3389/fnins.2017.00669.
[112] Chiang, S., Guindani, M., Yeh, H.J., Haneef, Z., Stern, J.M. and Vannucci, M. (2017). A Bayesian Vector Autoregressive Model for Multi-Subject Effective Connectivity Inference using Multi-Modal Neuroimaging Data. Human Brain Mapping, 38, 1311-1332.
[111] Wadsworth, D., Argiento, R., Guindani, M., Galloway-Pena, J., Shelburne, S.A. and Vannucci, M. (2017). An Integrative Bayesian Dirichlet-Multinomial Regression Model for the Analysis of Taxonomic Abundances in Microbiome Data. BMC Bioinformatics, 18:94, DOI 10.1186/s12859-017-1516-0.
2015-2016
[110] Zhang, L., Guindani, M., Versace, F., Engelmann, J.M. and Vannucci, M. (2016). A Spatio-Temporal Nonparametric Bayesian Model of Multi-Subject fMRI Data. Annals of Applied Statistics, 10(2), 638-666.
[109] Peterson, C.B., Stingo, F.C. and Vannucci, M. (2016). Joint Bayesian Variable and Graph Selection for Regression Models with Network-Structured Predictors. Statistics in Medicine, 35(7), 1017-1031.
[108] Villagran, A., Huerta, G., Vannucci, M., Jackson, C.S. and Nosedal, A. (2016). Non-Parametric Sampling Approximation via Voronoi Tessellations. Communications in Statistics – Simulation & Computation, 45, 1-20.
[107] Trevino, V., Cassese, A., Nagy, Z., Zhuang, X., Herbert, J., Antzack, P., Clarke, K., Davies, N., Rahman, A., Campbell, M., Guindani, M., Bicknell, R., Vannucci, M. and Falciani, F. (2016). A Network Biology Approach Identifies MolecularCross-talk between Normal Prostate Epithelial and Prostate Carcinoma Cells. PLoS Computational Biology, 12(4), e1004884.
[106] Li, Q., Dahl, D.B., Vannucci, M., Joo, H. and Tsai, J.W. (2016). KScons: A Bayesian Approach for Protein Residue Contact Prediction using the Knob-Socket Model of Protein Tertiary Structure. Bioinformatics, 32(24), 3774-3781.
[105] Chiang, S., Cassese, A., Guindani, M., Vannucci, M., Yeh, H.J., Haneef, Z. and Stern, J.M. (2016). Time-dependence of Graph Theory Metrics in Functional Connectivity Analysis. NeuroImage, 125, 601-615.
[104] Teo, I., Fronczyk, K., Guindani, M., Vannucci, M., Ulfers, S., Hanasono, M. and Fingeret, M.C. (2016). Salient Body Image and Psychosocial Concerns of Cancer Patients Undergoing Head and Neck Reconstruction. Head and Neck, 38(7), 1035-1042.
[103] Cassese, A., Guindani, M. and Vannucci, M. (2016). iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH data. In Statistical Analysis for High-Dimensional Data – The Abel Symposium 2014, Frigessi, A., Buhlmann, P., Glad, I., Langaas, M., Richardson, S. and Vannucci, M. (Eds). Springer Verlag, 105-123.
[102] 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.
[101] Cassese, A., Guindani, M., Antczak, P., Falciani, F. and Vannucci, M. (2015). A Bayesian Model for the Identification of Differentially Expressed Genes in Daphnia Magna Exposed to Munition Pollutants. Biometrics, 71, 803-811.
[100] Waters, A.E., Fronczyk, K., Guindani, M., Baraniuk, R.G. and Vannucci, M. (2015). A Bayesian Nonparametric Approach for the Analysis of Multiple Categorical Item Responses. Journal of Statistical Planning and Inference, 166, 52-66.
[99] Stingo, F.C., Swartz, M.D. and Vannucci, M. (2015). A Bayesian Approach for the Identification of Genes and Gene-level SNP Aggregates in a Genetic Analysis of Cancer Data. Statistics and Its Interface, 8(2), 137-151.
[98] Zhang, L., Guindani, M. and Vannucci, M. (2015). Bayesian Models for Functional Magnetic Resonance Imaging Data Analysis.WIREs Computational Statistics, 7, 21-41 (invited contribution).
[97] Fronczyk, K., Guindani, M. Hobbs, B.P., Ng, C. and Vannucci, M. (2015). A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization. Cancer Informatics, 14(S5), 151-162.
[96] Rembach, A., Stingo, F., Peterson, C., Vannucci, M., Do, K-A., Wilson, W.J., Macaulay, S.L., Ryan, T.M., Martins, R.N., Ames, D., Masters, C.L., Doecke, J.D. and the AIBL Research Group (2015). Bayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer’s Disease. Journal of Alzheimer’s Disease, 44(3), 917-925.
2013-2014
[95] Cassese, A., Guindani, M., Tadesse, M., Falciani, F. and Vannucci, M. (2014). A Hierarchical Bayesian Model for Inference on Copy Number Variants and their Association to Gene Expression. Annals of Applied Statistics, 8(1), 148-175.
[94] Zhang, L., Guindani, M., Versace, F. and Vannucci, M. (2014). A Spatio-Temporal Nonparametric Bayesian Variable Selection Model of fMRI Data for Clustering Correlated Time Courses. NeuroImage, 95, 162-175.
[93] Li, Q., Dahl, D.B., Vannucci, M., Joo, H. and Tsai, J.W. (2014). Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction. PLoS ONE, 9(10), e109832.
[92] Cassese, A., Guindani, A. and Vannucci, M. (2014). A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection. Cancer Informatics, 13(S2) 29-37.
[91] Cowley, A.W., Moreno, C.P., Jacob, H., Peterson, C.B., Stingo, F.C.,Ahn, K.W., Liu, P., Vannucci, M., Laud, P.W., Reddy, P., Lazar, J., Evans, L., Yang, C., Kurth, T. and Liang, M. (2014). Characterization of Biological Pathways Mediating a 1.37mbp Genomic Region Protective of Hypertension in Dahl S. Rats. Physiological Genomics, 46, 398-410.
[90] Fronczyk. K., Guindani, M., Vannucci, M., Palange, A. and Decuzzi, P. (2014). A Bayesian Hierarchical Model for Maximizing the Vascular Adhesion of Nanoparticles. Computational Mechanics, 53(3), 539-547.
[89] Shetty, A.N., Chiang, S., Maletic-Savatic, M., Kasprian, G., Vannucci, M. and Lee, W. (2014). Spatial Mapping of Translational Diffusion Coefficients Using Diffusion-Tensor Imaging: A Mathematical Description. Concepts in Magnetic Resonance Part A, 43(1), 1-27.
[88] Stingo, F.C., Guindani, M., Vannucci, M. and Calhoun, V. (2013). An Integrative Bayesian Modeling Approach to Imaging Genetics. Journal of the American Statistical Association, 108, 876-891.
[87] Jeong, J., Vannucci, M. and Ko, K. (2013). A Wavelet-based Bayesian Approach to Regression Models with Long Memory Errors and its Application to fMRI Data. Biometrics, 69(1), 184-196.
[86] Allen, G.I., Peterson, C.B., Vannucci, M. and Maletic-Savatic, M. (2013). Regularized Partial Least Squares with an Application to NMR Spectroscopy. Statistical Analysis and Data Mining, 6(4), 302-314.
[85] Peterson, C., 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. Statistics and Its Interface, 6, 547-558.
[84] Brownlees, C. and Vannucci, M. (2013). A Bayesian Approach for Capturing Daily Heterogeneity in Intradaily Durations Time Series. Studies in Nonlinear Dynamics and Econometrics, 17(1), 21-46.
[83] Swartz, M.D., Peterson, C.B., Lupo, P.J., Wu, X., Forman, M.R., Spitz, M.R., Hernandez, L.M., Vannucci, M. and Shete, S. (2013). Investigating Multiple Candidate Genes and Nutrients in the Folate Metabolism Pathway to Detect Genetic and Nutritional Risk Factors for Lung Cancer. PLoS ONE, 8(1), e53475.
[82] Day, R., Joo, H., Chavan, A., Lennox, K.P., Chen, Y.A., Dahl, D.B., Vannucci, M. and Tsai, J.W. (2013). Understanding the General Packing Rearrangements Required for Successful Template Based Modeling of Protein Structure from a CASP Experiment. Computational Biology and Chemistry, 42, 40-48.
[81] Yang, C., Stingo, F.C., Ahn, K.W., Liu, P., Vannucci, M., Laud, P.W., Skelton, M., O’Connor, P., Kurth, T., Moreno, C., Tsaih, S.W., Patone, G., Humme, O., Jacob, H.J., Liang, M. and Cowley, A.W. (2013). Increased Proliferative Cells in the Medullary Thick Ascending Limb of the Loop of Henle in the Dahl Salt-Sensitive Rat. Hypertension, 61(1), 208-215.
[80] Peterson, C.B., Swartz, M.D., Shete, S. and Vannucci, M. (2013). Bayesian Model Averaging for Genetic Association Studies. In Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data, Kim-Anh Do, Zhaohui Steve Qin and Marina Vannucci (Eds). Cambridge University Press, 208-223.
[79] Stingo, F.C. and Vannucci, M. (2013). Bayesian Models for Integrative Genomics. In Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data, Kim-Anh Do, Zhaohui Steve Qin and Marina Vannucci (Eds). Cambridge University Press, 272-291.
2011-2012
[78] Stingo, F.C., Vannucci, M. and Downey, G. (2012). Bayesian Wavelet-based Curve Classification via Discriminant Analysis with Markov Random Tree Priors. Statistica Sinica, 22, 465-488.
[77] Lee, S.H., Lim, J., Li, E., Vannucci, M. and Petkova, E. (2012). Order Test for High-Dimensional Two Sample Means. Journal of Statistical Planning and Inference, 142, 2719-2725.
[76] Flores, R.J., Li, Y., Yu, A., Shen, J., Lau, S.S., Rao, P.H., Vannucci, M., Lau, C.C. and Man, T.K. (2012). A Systems Biology Approach Reveals Common Metastatic Pathway in Osteosarcoma. BMC Systems Biology, 6:50.
[75] 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.
[74] 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.
[73] Kwon, D.W., Landi, M.T., Vannucci, M., Issaq, H.J., Prieto, D. and Pfeiffer, R.M. (2011). An Efficient Stochastic Search for Bayesian Variable Selection with High-Dimensional Correlated Predictors. Computational Statistics and Data Analysis, 55(10), 2807-2818.
[72] Chavan, A.G., Joo, H., Day, R., Lennox, K.P., Sukhanov, P., Dahl, D.B., Vannucci, M. and Tsai, J.W. (2011). Near-Native Protein Loop Modeling using Nonparametric Density Estimation Accommodating Sparcity. PLoS Computational Biology,7(10), e10002234.
[71] Stingo, F.C. and Vannucci, M. (2011). Variable Selection for Discriminant Analysis with Markov Random Field Priors for the Analysis of Microarray Data. Bioinformatics, 27(4), 495-501.
[70] Trevino, V., Tadesse, M.G., Vannucci, M., Al-Shahrour, F., Antczak, P., Durant, S., Bikfalvi, A., Dopazo, J., Campbell, M.J. and Falciani, F. (2011). Analysis of normal-tumour tissue interaction in tumours: Prediction of prostate cancer features from the molecular profile of adjacent normal cells. PLoS ONE, 6(3), e16492.
[69] Preter, M., Lee, S.H., Petkova. E., Vannucci, M., Kim, S. and Klein, D.F. (2011). Controlled Cross-over Study in Normal Subjects of Naloxone-preceding-lactate Infusions; Respiratory and Subjective Responses: Relationship to Endogenous Opioid System, Suffocation False Alarm Theory and Childhood Parental Loss. Psychological Medicine, 41(2), 385-394.
[68] Cho, Y., Kim, H., Turner, N.D., Mann, J.C., Wei, J., Taddeo, S.S., Davidson, L.A., Wang, N., Vannucci, M., Carroll, R.J., Chapkin, R.S. and Lupton, J.R. (2011). A Chemoprotective Fish Oil/Pectin Diet Temporally Alters Gene Expression Profiles in Exfoliated Rat Colonocytes Throughout Oncogenesis. Journal of Nutrition, 141(6), 1029-35.
[67] Vannucci, M. and Stingo, F.C. (2011). Bayesian Models for Variable Selection that Incorporate Biological Information (with discussion). In Bayesian Statistics 9 (J.M. Bernardo, M.J. Bayarri, J.O. Berger, A.P. Dawid, D. Heckerman, A.F.M. Smith and M. West eds.). Oxford: University Press, 659-678.
2009-2010
[66] Stingo, F.C., Chen, Y.A., Vannucci, M., Barrier, M. and Mirkes, P.E. (2010). A Bayesian Graphical Modeling Approach to MicroRNA Regulatory Network Inference. Annals of Applied Statistics, 4(4), 2024-2048.
[65] Lennox, K.P., Dahl, D.B., Vannucci, M., Day, R. and Tsai, J.W. (2010). A Dirichlet Process Mixture of Hidden Markov Models for Protein Structure Prediction. Annals of Applied Statistics, 4(2), 916-942.
[64] Zhu, H., Vannucci, M. and Cox, D.D. (2010). A Bayesian Hierarchical Model for Classification with Selection of Functional Predictors. Biometrics, 66(2), 463-473.
[63] Savitsky, T. and Vannucci, M. (2010). Spiked Dirichlet Process Priors for Gaussian Process Models. Journal of Probability and Statistics, 2010, article ID 201489, 14 pages.
[62] Koshelev, M., Lohrenz, T., Vannucci, M. and Montague, P.R. (2010). Biosensor Approach to Psychopathology Classification. PLoS Computational Biology, 6(10), e1000966.
[61] Day, R., Lennox, K.P., Dahl, D.B., Vannucci, M. and Tsai, J.W. (2010). Characterizing the Regularity of Tetrahedral Packing Motifs in Protein Tertiary Structure. Bioinformatics, 26(24), 3059-3066.
[60] Guo, B., Villagran, A., Vannucci, M., Wang, J., Davis, C., Man, T.K., Lau, C. and Guerra, R. (2010). Bayesian Estimation of Genomic Copy Number with Single Nucleotide Polymorphism Genotyping Arrays. BMC Research Notes, 3:350.
[59] Zreik, T.G., Mazloom, A., Chen, Y., Vannucci, M., Fulton, S., Hadziahmetovic, M., Asmar, N., Munkarah, A.R., Ayoub, C.M., Shihadeh, F., Berjawi, G., Hannoun, A., Zalloua, P., Wogan, C. and Dabaja, B. (2010). Fertility Drugs and the Risk of Breast Cancer: A Meta-Analysis and Review. Breast Cancer Research and Treatment, 124(1), 13-26.
[58] Jeong, J., Vannucci, M., Do, K.-A., Broom, B., Kim, S., Sha, N., Tadesse, M., Yan, K. and Pusztai, L. (2010). Gene Selection for the Identification of Biomarkers in High-Throughput Data. In Bayesian Modeling in Bioinformatics, Dipak K. Dey, Samiran Ghosh and Bani Mallick (Eds). Chapman and Hall/CRC press, 233-254.
[57] Lennox, K., Dahl, D.B., Vannucci, M. and Tsai, J. (2009). Density Estimation for Protein Conformation Angles using a von Mises Distribution and Bayesian Nonparametrics. Journal of the American Statistical Association, 104, 586-596. Correction in 104, 1728.
[56] Kim, S., Dahl, D.B. and Vannucci, M. (2009). Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models. Bayesian Analysis, 4(4), 707-732.
[55] Ko, K., Qu, L. and Vannucci, M. (2009). Wavelet-based Bayesian Estimation of Partially Linear Regression Models with Long Memory Errors. Statistica Sinica, 19(4), 1463-1478.
[54] Gardoni, P., Trejo, D., Vannucci, M. and Bhattacharjee, C. (2009). Probability Models for the Modulus of Elasticity of Self Consolidated Concrete: A Bayesian Approach. ASCE Journal of Engineering Mechanics, 135, 295-306.
[53] Small, C.M., Carney, G.E., Mo, Q., Vannucci, M. and Jones, A.G. (2009). A Microarray Analysis of Sex- and Gonad-biased Gene Expression in the Zebrafish: Evidence for Masculinization of the Transcriptome. BMC Genomics, 10:579.
[52] Swanson, R., Vannucci, M. and Tsai, J. (2009). Information Theory Provides a Comprehensive Framework for the Evaluation of Protein Structure Predictions. Proteins: Structure, Function, and Bioinformatics, 74(3), 701-711.
[51] Jayaraman, A., Maguire, T., Vemula, M., Kwon, D.W., Vannucci, M., Berthiaume, F., and Yarmush, M.L. (2009). Gene Expression Profiling of Long-Term Changes in Rat Liver Following Burn-Injury. Journal of Surgical Research, 152(1), 3-17.
[50] Popovic, N., Bridenbaugh, E.A., Neiger, J.D., Hu, J.J., Vannucci, M., Mo, Q., Trzeciakowski, J., Miller, M.W., Fossum, T.W., Humphrey, J.D. and Wilson, E. (2009). Transforming Growth Factor Beta Signaling in Hypertensive Remodeling of Porcine Aorta. American Journal of Physiology: Heart and Circulatory Physiology, 297, 2044-2053.
2005-2008
[49] Lee, S., Lim, J., Vannucci, M., Petkova, E., Preter, M. and Klein, D.F. (2008). Order-Preserving Dimension Reduction Test for the Dominance of Two Mean Curves with Application to Tidal Volume Curves. Biometrics, 64(3), 931-939.
[48] Ortega, F., Semeith, K., Turan, N., Compton, R., Trevino, V., Vannucci, M. and Falciani, F. (2008). Models and Computational Strategies Linking Physiological Response to Molecular Networks from Large-Scale Data. Philosophical Transactions of the Royal Society A, 366, 3067-3089.
[47] Dahl, D.B., Mo, Q. and Vannucci, M. (2008). Simultaneous Inference for Multiple Testing and Clustering via a Dirichlet Process Mixture Model. Statistical Modelling: An International Journal,8(1), 23-39.
[46] 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.
[45] Kwon, D.W., Vannucci, M., Song, J.J., Jeong, J. and Pfeiffer, R. (2008). A Novel Wavelet-based Thresholding Method for the Pre-Processing of Mass Spectrometry Data that Accounts for Heterogeneous Noise. Proteomics, 8(15), 3019-3029.
[44] Cruz-Marcelo, A., Guerra, R., Vannucci, M., Li, Y., Lau, C. and Man, C. (2008). Comparison of Algorithms for Pre-Processing of SELDI-TOF Mass Spectrometry Data. Bioinformatics, 24(19), 2129-2136.
[43] Dahl, D., Bohannan, Z., Mo, Q., Vannucci, M. and Tsai, J. (2008). Assessing Side-Chain Perturbations of the Protein Backbone: A Knowledge based Classification of Residue Ramachandran Space. Journal of Molecular Biology, 378, 749-758.
[42] Kagiampakis, I., Jin, H., Kim, S, Vannucci, M., LiWang, P.J. and Tsai, J. (2008). Conservation of Unfavorable Sequence Motifs that Contribute to Chemokine Quaternary State. Biochemistry, 47, 10637-10648.
[41] Di Martino, A., Ghaffari, M., Curchack, J., Philip Reiss, P., Hyde, C., Vannucci, M., Petkova, E., Klein, D.F. and Castellanos, F.X. (2008). Decomposing Intra-Subject Variability in Children with Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 64(7), 607-614.
[40] Kwon, D.W., Tadesse, M.G., Sha, N., Pfeiffer, R.M. and Vannucci, M. (2007). Identifying Biomarkers from Mass Spectrometry Data with Ordinal Outcome. Cancer Informatics, 3, 19-28.
[39] Kim, S., Tsai, J., Kagiampakis, I., LiWang, P. and Vannucci, M. (2007). Detecting Protein Dissimilarities in Multiple Alignments using Bayesian Variable Selection. Bioinformatics, 23(2), 245-246.
[38] Alhamad, M.N., Stuth, J. and Vannucci, M. (2007). Biophysical Modeling and NDVI Time Series to Project Near-Term Forage Supply: Spectral Analysis Aided by Wavelet Denoising and ARIMA Modeling. International Journal of Remote Sensing, 28(11), 2513-2548.
[37] Kim, S., Tadesse, M.G. and Vannucci, M. (2006). Variable Selection in Clustering via Dirichlet Process Mixture Models. Biometrika, 93(4), 877-893.
[36] Ko, K. and Vannucci, M. (2006). Bayesian Wavelet Analysis of Autoregressive Fractionally Integrated Moving-Average Processes. Journal of Statistical Planning and Inference, 136(10), 3415-3434.
[35] Sha, N., Tadesse, M.G. and Vannucci, M. (2006). Bayesian Variable Selection for the Analysis of Microarray Data with Censored Outcome. Bioinformatics, 22(18), 2262-2268.
[34] Kwon, D.W., Ko, K., Vannucci, M., Reddy, A.L.N. and Kim, S. (2006). Wavelet Methods for the Detection of Anomalies and their Application to Network Traffic Analysis. Quality and Reliability Engineering International, 22, 1-17.
[33] Ko, K. and Vannucci, M. (2006). Bayesian Wavelet-based Methods for the Detection of Multiple Changes of the Long Memory Parameter. IEEE Transactions on Signal Processing, 54(11), 4461-4470.
[32] Tadesse, M.G., Sha, N., Kim, S. and Vannucci, M. (2006). Identification of Biomarkers in Classification and Clustering of High-Throughput Data. In Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Mueller and Marina Vannucci (Eds). Cambridge University Press, 97-115.
[31] Kwon, D.W., Kim, S., Dahl, D., Swartz, M., Tadesse, M.G. and Vannucci, M. (2006). Identification of DNA Regulatory Motifs and Regulators by Integrating Gene Expression and Sequence Data. In Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Mueller and Marina Vannucci (Eds). Cambridge University Press, 333-346.
[30] 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.
[29] Park, C.G., Vannucci, M. and Hart, J.D. (2005). Bayesian Methods for Wavelet Series in Single-Index Models. Journal of Computational and Graphical Statistics, 14(4), 770-794.
[28] Tadesse, M.G., Ibrahim, J.G., Vannucci, M. and Gentleman, R. (2005). Wavelet Thresholding with Bayesian False Discovery Rate Control. Biometrics, 61, 25-35.
[27] Fabbroni, L., Vannucci, M., Cuoco, E., Losurdo, G., Mazzoni, M. and Stanga, R. (2005). Wavelet Tests for the Detection of Transients in the VIRGO Interferometric Gravitational Wave Detector. IEEE Transactions on Instrumentation and Measurement, 54(1), 151-162.
[26] Vannucci, M., Sha, N. and Brown, P.J. (2005). NIR and Mass Spectra Classification: Bayesian Methods for Wavelet-based Feature Selection. Chemometrics and Intelligent Laboratory Systems, 77, 139-148.
2001-2004
[25] Sha, N., Vannucci, M., Tadesse, M.G., Brown, P.J., Dragoni, I., Davies, N., Roberts, T.C., Contestabile, A., Salmon, N., Buckley, C. and Falciani, F. (2004). Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage. Biometrics, 60(3), 812-819.
[24] Gabbanini, F., Vannucci, M., Bartoli, G. and Moro, A. (2004). Wavelet Packet Methods for the Analysis of Variance of Time Series with Application to Crack Widths on the Brunelleschi Dome. Journal of Computational and Graphical Statistics, 13(3), 639-658.
[23] Tadesse, M.G., Vannucci, M. and Lio, P. (2004). Identification of DNA Regulatory Motifs using Bayesian Variable Selection. Bioinformatics, 20(16), 2553-2561.
[22] Davies, N., Tadesse, M.G., Vannucci, M., Kikuchi, H., Trevino, V., Sarti, D., Dragoni, I., Contestabile, A., Zanders, E. and Falciani, F. (2004). Making Sense of Molecular Signatures in the Immune System. Journal of Combinatorial Chemistry and High Throughput Screening, 7(3), 231-238.
[21] Kim, S.S., Reddy, A.L.N. and Vannucci, M. (2004). Detecting Traffic Anomalies Through Aggregate Analysis of Packet Header Data. In Proceedings of the 3rd IFIP-TC6 Networking conference. Mitrou, N. et al. (Editors), Lecture Notes in Computer Science, vol. 3042, Springer Verlag, 1047-1059 (refereed volume, 103/539=19.1% acceptance rate).
[20] Kim, S.S., Reddy, A.L.N. and Vannucci, M. (2004). Detecting Traffic Anomalies Using Discrete Wavelet Transform. In Proceedings of the International Conference on Information Networking. Kahng, H.K. and Goto, S. (Editors), Lecture Notes in Computer Science, vol. 3090, Springer Verlag, 951-961 (refereed volume, 104/341=30.5% acceptance rate).
[19] Morris, J.S., Vannucci, M., Brown, P.J. and Carroll, R.J. (2003). Wavelet-Based Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis (with discussion). Journal of the American Statistical Association, 98, 573-597.
[18] Vannucci, M., Brown, P.J. and Fearn, T. (2003). A Decision Theoretical Approach to Wavelet Regression on Curves with a High Number of Regressors. Journal of Statistical Planning and Inference, 112(1-2), 195-212.
[17] Sha, N., Vannucci, M., Brown, P.J., Trower, M.K., Amphlett, G. and Falciani, F. (2003). Gene Selection in Arthritis Classification with Large-Scale Microarray Expression Profiles. Comparative and Functional Genomics, 4(2), 171-181.
[16] Lee, K.E., Sha, N., Dougherty, E., Vannucci, M. and Mallick, B.K. (2003). Gene Selection: A Bayesian Variable Selection Approach. Bioinformatics, 19(1), 90-97.
[15] Lio, P. and Vannucci, M. (2003). Investigating the Evolution and Structure of Chemokine Receptors. Gene, 317, 29-37.
[14] Brown, P.J., Vannucci, M. and Fearn, T. (2002). Bayes Model Averaging with Selection of Regressors. Journal of the Royal Statistical Society, Series B, 64(3), 519-536.
[13] 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.
[12] Vannucci, M. and Lio, P. (2001). Non-Decimated Wavelet Analysis of Biological Sequences: Applications to Protein Structure and Genomics. Sankhya, Series B, 63(2), 218-233.
[11] Vannucci, M., Brown, P.J. and Fearn, T. (2001). Predictor Selection for Model Averaging. In Bayesian methods with applications to science, policy and official statistics. (Eds E.I. George and P. Nanopoulos), Eurostat: Luxemburg, 553-562.
1996-2000
[10] Lio, P. and Vannucci, M. (2000). Wavelet Change-Point Prediction of Transmembrane Proteins. Bioinformatics, 16(4), 376-382.
[9] Lio, P. and Vannucci, M. (2000). Finding Pathogenicity Islands and Gene Transfer Events in Genome Data. Bioinformatics, 16(10), 932-940.
[8] Spiegelman, C., Bennett, J., Vannucci, M., McShane, M.J. and Cote, G. (2000). A Transparent Tool for Seemingly Difficult Calibrations: The Parallel Calibration Method. Analytical Chemistry, 72(1), 135-140. Correction in 72(8), p. 1944.
[7] Brown, P.J., Fearn, T. and Vannucci, M. (1999). The Choice of Variables in Multivariate Regression: A Non-Conjugate Bayesian Decision Theory Approach. Biometrika, 86(3), 635-648.
[6] Vannucci, M. and Corradi, F. (1999). Covariance Structure of Wavelet Coefficients: Theory and Models in a Bayesian Perspective. Journal of the Royal Statistical Society, Series B, 61(4), 971-986.
[5] Vannucci, M. and Corradi, F. (1999). Modeling Dependence in the Wavelet Domain. In Bayesian Inference in Wavelet based Models. (Eds P. Muller and B. Vidakovic), New York: Springer-Verlag, 173-186.
[4] 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.
[3] Brown, P.J., Vannucci, M. and Fearn, T. (1998). Bayesian Wavelength Selection in Multicomponent Analysis. Journal of Chemometrics, 12(3), 173-182.
[2] Vannucci, M. and Vidakovic, B. (1997). Preventing the Dirac Disaster: Wavelet based Density Estimation. Journal of the Italian Statistical Society, 6(2), 145-159.
[1] Brown, P.J., Vannucci, M. and Fearn, T. (1997). Multivariate Bayesian Wavelength Selection for NIR Spectra Applied to Biscuit Dough Pieces. Proceedings of the 5a Journees Europeennes Agro-Industrie et Methodes Statistique, 19.1-19.11. (refereed volume).
