@article{nokey,

title = {A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data},

author = {Denti, Francesco; Camerlenghi, Federico; Guindani, Michele; Mira, Antonietta},

url = {https://arxiv.org/abs/2008.07077},

year  = {2023},

date = {2023-03-01},

journal = { Journal of the American Statistical Association,},

pages = {405-416},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A nonparametric Bayesian model for estimating spectral densities of resting‐state EEG twin data},

author = {Brian Hart, Michele Guindani, Stephen Malone and Mark Fiecas},

url = {http://space.micheleguindani.info/papers/NBDP_FinalRevision.pdf



},

doi = {https://doi.org/10.1111/biom.13393},

year  = {2022},

date = {2022-01-01},

journal = {Biometrics},

volume = {78},

issue = {1},

pages = {313-323},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P74,

title = {Two-group Poisson-Dirichlet mixtures for multiple testing},

author = {Francesco Denti and Michele Guindani and Fabrizio Leisen and Antonio Lijoi and William Duncan Wadsworth and Marina Vannucci},

url = {http://space.micheleguindani.info/papers/TwoGroupsBNP.pdf},

doi = {10.1111/biom.13314},

year  = {2021},

date = {2021-06-01},

urldate = {2021-06-01},

journal = {Biometrics},

volume = {77},

number = {2},

pages = {622-633},

abstract = {Abstract The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Here, we investigate employing mixtures of two-parameter Poisson-Dirichlet Processes instead, and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs nonlocal prior densities to allow separation between the two mixture components. We obtain a closed-form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward Markov Chain Monte Carlo implementation. We compare the performance of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer data set and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate-to-severe diarrhea.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P61,

title = {A Bayesian nonparametric spiked process prior for dynamic model selection},

author = {A Cassese and W Zhu and M } {bf Guindani and M Vannucci},

url = {https://projecteuclid.org/download/pdfview_1/euclid.ba/1533866669},

doi = {DOI: 10.1214/18-BA1116},

year  = {2019},

date = {2019-01-01},

journal = {Bayesian Analysis},

volume = {14},

number = {2},

pages = {553-572},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P62,

title = {Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data},

author = {Q Li and A Cassese and M Guindani and M Vannucci},

url = {https://pubmed.ncbi.nlm.nih.gov/30125947/},

year  = {2019},

date = {2019-01-01},

journal = {Biometrics},

volume = {75},

number = {1},

pages = {183-192},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P63,

title = {Filtering and estimation for a class of stochastic volatility models with intractable likelihoods},

author = {E R Vankov and M } {bf Guindani and K B Ensor},

url = {https://projecteuclid.org/journals/bayesian-analysis/volume-14/issue-1/Filtering-and-Estimation-for-a-Class-of-Stochastic-Volatility-Models/10.1214/18-BA1099.full},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Bayesian Analysis},

volume = {14},

number = {1},

pages = {29-52},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P60,

title = {Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity},

author = {S Chiang and E R Vankov and H J Yeh and M } {bf Guindani and M Vannucci and Z Haneef and J M Stern},

url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190220},

year  = {2018},

date = {2018-01-01},

journal = {PLoS ONE},

volume = {13},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P47,

title = {A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints},

author = {Q Li and M. Guindani and B J Reich and H D Bondell and M Vannucci},

url = {http://space.micheleguindani.info/papers/Li_et_al-2017.pdf},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Statistical Analysis and Data Mining},

volume = {10},

number = {6},

pages = {393-409},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P55,

title = {A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection},

author = {Sharon Chiang and Michele Guindani and Hsiang J Yeh and Sandra Dewar and Zulfi Haneef and John M Stern and Marina Vannucci},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5723403/},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Frontiers in Neuroscience},

volume = {11},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P48,

title = {An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data},

author = {W D Wadsworth and R Argiento and M. Guindani and J Galloway-Pena and S A Shelbourne and M Vannucci},

url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1516-0},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {BMC Bioinformatics},

volume = {18},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P49,

title = {Bayesian nonparametric statistics: A new toolkit for discovery in cancer research},

author = {P F Thall and P Mueller and Y Xu and M Guindani},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5681362/},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Pharmaceutical Statistics},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P50,

title = {Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data},

author = {S Chiang and M Guindani and H J Yeh and Z Haneef and J M Stern and M Vannucci},

url = {https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.23456},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Human Brain Mapping},

volume = {38},

number = {3},

pages = {1311-1332},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P35,

title = {Bayesian predictive model for imaging genetics with application to schizophrenia},

author = {T Chekouo and F C Stingo and M Guindani and K -A Do},

url = {http://space.micheleguindani.info/papers/Chekou_AOAS_2016.pdf},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Annals of Applied Statistics},

volume = {10},

number = {3},

pages = {1547-1571},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P37,

title = {A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data},

author = {L Zhang and M Guindani and F Versace and J M Engelmann and M Vannucci},

url = {http://space.micheleguindani.info/papers/Zhang_AOAS926_Journal.pdf

http://space.micheleguindani.info/papers/Zhang_AOAS926-sup.pdf

},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Annals of Applied Statistics},

volume = {10},

number = {2},

pages = {638-666},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P27,

title = {A Bayesian nonparametric approach for the analysis of multiple categorical item responses},

author = {A Waters and K Fronczyk and M Guindani and R G Baraniuk and M Vannucci},

url = {http://space.micheleguindani.info/papers/Waters et al-JSPI-2015.pdf},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Journal of Statistical Planning and Inference},

volume = {166},

pages = {52-66},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P30,

title = {Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome},

author = {R Graziani and M Guindani and P F Thall},

url = {http://space.micheleguindani.info/papers/Graziani_et_al-2015-Biometrics.pdf

http://space.micheleguindani.info/papers/Graziani_Biom_2015-supp.pdf},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Biometrics},

volume = {71},

number = {1},

pages = {188-197},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P31,

title = {False discovery control in large-scale spatial multiple testing},

author = {W Sun and B J Reich and T Tony Cai and M Guindani and A Schwartzman},

url = {http://space.micheleguindani.info/papers/Sun_et_al-2015.pdf

http://space.micheleguindani.info/papers/Sun_et_al-2015-sup.pdf},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},

volume = {77},

number = {1},

pages = {59-83},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P13,

title = {A Bayesian semiparametric approach for the differential analysis of sequence counts data},

author = {M Guindani and N Sep{'u}lveda and C D Paulino and P M\"{u}ller},

url = {http://space.micheleguindani.info/papers/Guindani_SequenceCountsData.pdf

http://space.micheleguindani.info/papers/Guindani_SequenceCountsData_suppl.pdf},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Journal of the Royal Statistical Society. Series C: Applied Statistics},

volume = {63},

number = {3},

pages = {385-404},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P14,

title = {A hierarchical Byesian model for inference of copy number variants and their association to gene expression},

author = {A Cassese and M Guindani and M G Tadesse and F Falciani and M Vannucci},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4018204/},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Annals of Applied Statistics},

volume = {8},

number = {1},

pages = {148-175},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P16,

title = {A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses},

author = {L Zhang and M Guindani and F Versace and M Vannucci},

url = {https://www.sciencedirect.com/science/article/abs/pii/S1053811914001712?via%3Dihub},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {NeuroImage},

volume = {95},

pages = {162-175},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P23,

title = {Generalized Species Sampling Priors With Latent Beta Reinforcements},

author = {E M Airoldi and T Costa and F Bassetti and F Leisen and M Guindani},

url = {http://space.micheleguindani.info/papers/BetaGOS-journal.pdf

http://space.micheleguindani.info/papers/BetaGOS-suppl.pdf},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Journal of the American Statistical Association},

volume = {109},

number = {508},

pages = {1466-1480},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P7f,

title = {An integrative Bayesian modeling approach to imaging genetics},

author = {F C Stingo and M } {bf Guindani and M Vannucci and V D Calhoun},

url = {http://space.micheleguindani.info/papers/Stingo_Guindani_Vannucci_Calhoun.pdf},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {Journal of the American Statistical Association},

volume = {108},

number = {503},

pages = {876-891},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P3k,

title = {A Bayesian discovery procedure},

author = {M Guindani and P M\"{u}ller and S Zhang},

url = {http://space.micheleguindani.info/papers/BDP.pdf},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},

volume = {71},

number = {5},

pages = {905-925},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P4k,

title = {Hybrid Dirichlet mixture models for functional data},

author = {S Petrone and M } {bf Guindani and A E Gelfand},

url = {http://space.micheleguindani.info/papers/HybridDP.pdf},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},

volume = {71},

number = {4},

pages = {755-782},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P2k,

title = {Generalized spatial Dirichlet process models},

author = {J A Duan and M } {bf Guindani and A E Gelfand},

url = {http://space.micheleguindani.info/papers/Generalized_spatial_technicalreport.pdf},

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

journal = {Biometrika},

volume = {94},

number = {4},

pages = {809-825},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{P1k,

title = {Smoothness properties and gradient analysis under spatial Dirichlet process models},

author = {M Guindani and A E Gelfand},

url = {http://space.micheleguindani.info/papers/SmoothnessProperties.pdf},

year  = {2006},

date = {2006-01-01},

urldate = {2006-01-01},

journal = {Methodology and Computing in Applied Probability},

volume = {8},

number = {2},

pages = {159-189},

keywords = {},

pubstate = {published},

tppubtype = {article}

}