More nonparametric Bayesian inference in applications

• Airoldi E, Costa T, Bassetti F, Guindani M, Leisen F (2014) Generalized species sampling priors with latent beta reinforcements. J Am Stat Assoc 109(508):1466–1480

  Article  MathSciNet  MATH  Google Scholar 

• Bassetti F, Crimaldi I, Leisen F (2010) Conditionally identically distributed species sampling sequences. Adv Appl Probab 42(2):433–459

  Article  MathSciNet  MATH  Google Scholar 

• Berti P, Pratelli L, Rigo P (2004) Limit theorems for a class of identically distributed random variables. Ann Probab 32(3):2029–2052

  MathSciNet  MATH  Google Scholar 

• Branscum AJ, Johnson WO, Hanson TE, Gardner IA (2008) Bayesian semiparametric ROC curve estimation and disease diagnosis. Stat Med 27(13):2474–2496

  Article  MathSciNet  Google Scholar 

• Branscum AJ, Johnson WO, Hanson TE, Baron AT (2015) Flexible regression models for ROC and risk analysis, with or without a gold standard. Stat Med 34(30):3997–4015

  Article  MathSciNet  Google Scholar 

• Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, Kuo WL, Lapuk A, Neve RM, Qian Z, Ryder T, Chen F, Feiler H, Tokuyasu T, Kingsley C, Dairkee S, Meng Z, Chew K, Pinkel D, Jain A, Ljung BM, Esserman L, Albertson DG, Waldman FM, Gray JW (2006) Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10(6):529–541

  Article  Google Scholar 

• De Iorio M, Johnson WO, Müller P, Rosner GL (2009) Bayesian nonparametric non-proportional hazards survival modelling. Biometrics 65(3):762–771

  Article  MathSciNet  MATH  Google Scholar 

• Do K, Müller P, Tang F (2005) A Bayesian mixture model for differential gene expression. J R Stat Soc Ser C 54(3):627–644

  Article  MathSciNet  MATH  Google Scholar 

• Du L, Chen M, Lucas J, Carlin L (2010) Sticky hidden Markov modelling of comparative genomic hybridization. IEEE Trans Signal Process 58(10):5353–5368

  Article  MathSciNet  Google Scholar 

• Durante D, Dunson DB, Vogelstein JT (2016) Nonparametric Bayes modeling of populations of networks. J Am Stat Assoc. doi:10.1080/01621459.2016.1219260

• Efron B (2004) Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. J Am Stat Assoc 99(465):96–104

  Article  MathSciNet  MATH  Google Scholar 

• Flournoy N, May C, Secchi P (2012) Asymptotically optimal response-adaptive designs for allocating the best treatment: an overview. Int Stat Rev 80(2):293–305

  Article  MathSciNet  Google Scholar 

• Fortini S, Petrone S, Sporysheva P (2016) On a notion of partially conditionally identically distributed sequences. Technical report arXiv:1608.00471

• Fox E, Sudderth E, Jordan M, Willsky A (2011) A sticky HDP-HMM with application to speaker diarization. Ann Appl Stat 5(2A):1020–1056

  Article  MathSciNet  MATH  Google Scholar 

• Friston KJ (2011) Functional and effective connectivity: a review. Brain Connect 1(1):13–36

  Article  MathSciNet  Google Scholar 

• Geisser S, Eddy WF (1979) A predictive approach to model selection. J Am Stat Assoc 74(365):153–160

  Article  MathSciNet  MATH  Google Scholar 

• Gelfand A, Kottas A, MacEachern S (2005) Bayesian nonparametric spatial modeling with Dirichlet processes mixing. J Am Stat Assoc 100:1021–1035

  Article  MATH  Google Scholar 

• Guindani M, Müller P, Zhang S (2009) A Bayesian discovery procedure. J R Stat Soc B 71(5):905–925

  Article  MathSciNet  Google Scholar 

• Guindani M, Sepúlveda N, Paulino CD, Müller P (2014) A Bayesian semiparametric approach for the differential analysis of sequence counts data. J R Stat Soc Ser C (Appl Stat) 63(3):385–404

  Article  MathSciNet  Google Scholar 

• Hanson T, Johnson WO (2002) Modeling regression error with a mixture of Polya trees. J Am Stat Assoc 97(460):1020–1033

  Article  MathSciNet  MATH  Google Scholar 

• Hanson T, Johnson WO (2004) A Bayesian semiparametric AFT model for interval-censored data. J Comput Graph Stat 13(2):341–361

  Article  MathSciNet  Google Scholar 

• Hanson T, Johnson W, Laud P (2009) Semiparametric inference for survival models with step process covariates. Can J Stat 37(1):60–79

  Article  MathSciNet  MATH  Google Scholar 

• Hanson T, Branscum A, Johnson W (2011) Predictive comparison of joint longitudinal-survival modeling: a case study illustrating competing approaches (with discussion). Lifetime Data Anal 17:3–18

• He Y (2014) Bayesian cluster analysis with longitudinal data. Ph.D. thesis, Department of Statistics, University of California, Irvine

• Hu F, Zhang LX (2004) Asymptotic properties of doubly adaptive biased coin designs for multitreatment clinical trials. Ann Stat 32(1):268–301

  MathSciNet  MATH  Google Scholar 

• Jbabdi S, Woolrich M, Behrens T (2009) Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. NeuroImage 44(2):373–384

  Article  Google Scholar 

• Johnson W, de Carvalho M (2015) Bayesian nonparametric biostatistics. In: Mitra R, Müller P (eds) Nonparametric Bayesian methods in biostatistics and bioinformatics. Springer, New York, pp 15–53

  Chapter  Google Scholar 

• Kim S, Dahl DB, Vannucci M (2009) Spiked Dirichlet process prior for Bayesian multiple hypothesis testing in random effects models. Bayesian Anal 4(4):707–732

  Article  MathSciNet  MATH  Google Scholar 

• Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38(4):963–974

  Article  MATH  Google Scholar 

• Li Y, Lin X, Müller P (2010) Bayesian inference in semiparametric mixed models for longitudinal data. Biometrics 66(1):70–78

  Article  MathSciNet  MATH  Google Scholar 

• Li F, Zhang T, Wang Q, Gonzalez M, Maresh E, Coan J (2015) Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression. Ann Appl Stat 9(12):687–713

  Article  MathSciNet  MATH  Google Scholar 

• Muliere P, Paganoni AM, Secchi P (2006) A randomly reinforced urn. J Stat Plan Inference 136(6):1853–1874

  Article  MathSciNet  MATH  Google Scholar 

• Müller P, Parmigiani G, Robert CP, Rousseau J (2004) Optimal sample size for multiple testing: the case of gene expression microarrays. J Am Stat Assoc 99:990–1001

  Article  MathSciNet  MATH  Google Scholar 

• Müller P, Parmigiani G, Rice K (2007) FDR and Bayesian multiple comparisons rules. In: Bernardo J, Bayarri M, Berger J, Dawid A, Heckerman D, Smith A, West M (eds) Bayesian Stat 8. Oxford University Press, Oxford

  Google Scholar 

• Norris M, Johnson W, Gardner I (2014) A semiparametric model for bivariate longitudinal diagnostic outcome data in the absence of a gold standard. Stat Interface 7:417–438

  Article  MathSciNet  Google Scholar 

• Quintana FA, Johnson WO, Waetjen LE, Gold EB (2016) Bayesian nonparametric longitudinal data analysis. J Am Stat Assoc 111(515):1168–1181

  Article  MathSciNet  Google Scholar 

• Shahbaba B, Johnson WO (2013) Bayesian nonparametric variable selection as an exploratory tool for discovering differentially expressed genes. Stat Med 32(12):2114–2126

  Article  MathSciNet  Google Scholar 

• Sun W, Reich BJ, Tony Cai T, Guindani M, Schwartzman A (2015) False discovery control in large-scale spatial multiple testing. J R Stat Soc Ser B (Stat Methodol) 77(1):59–83

  Article  MathSciNet  Google Scholar 

• Teh YW, Jordan MI, Beal MJ, Blei DM (2006) Hierarchical Dirichlet processes. J Am Stat Assoc 101(476):1566–1581

  Article  MathSciNet  MATH  Google Scholar 

• Zeger SL, Diggle PJ (1994) Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters. Biometrics 50(3):689–699

  Article  MATH  Google Scholar 

• Zhang L, Guindani M, Versace F, Vannucci M (2014) A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses. NeuroImage 95:162–175

  Article  Google Scholar 

• Zhang L, Guindani M, Versace F, Engelmann JM, Vannucci M (2016) A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data. Ann Appl Stat 10(2):638–666

  Article  MathSciNet  MATH  Google Scholar 

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