Papers

• L. Aiello, M. Fontana, A. Guglielmi (2023+). Bayesian Functional Emulation of CO2 Emissions on Future Climate Change Scenarios. Environmetrics, Early View, DOI: 10.1002/env.2821


• M. De Iorio, S. Favaro, A. Guglielmi, L. Ye (2023). Bayesian Nonparametric Mixture Modeling for Temporal Dynamics of Gender Stereotypes. The Annals of Applied Statistics, Next Issues, DOI: 10.1214/22-AOAS1717


• Y. Ren, A. Guglielmi, L. Maestripieri (2023). Gender Inequalities at Work in Southern Europe. Metron, Latest articles, DOI 10.1007/s40300-023-00245-4


• A. Mozden, A. Cremaschi, A. Cadonna, A. Guglielmi, G. Kastner (2022). Bayesian modeling and clustering for spatio-temporal areal data: an application to Italian unemployment. Spatial Statistics, Vol. 52, 100715


• M. Beraha, R. Argiento, J. Møller, A. Guglielmi (2022). MCMC computations for Bayesian mixture models using repulsive point processes. Journal of Computational and Graphical Statistics, Vol. 31, 422--435


• M. Beraha, M. Pegoraro, R. Peli, A. Guglielmi (2021). Spatially dependent mixture models via the Logistic Multivariate CAR prior. Spatial Statistics, Vol. 46, 100548


• M. Beraha, A. Guglielmi, F. A. Quintana (2021). The Semi-Hierarchical Dirichlet process and its application to clustering homogeneous distributions. Bayesian Analysis, Vol. 16, 1187--1219


• D. Messenio, A. Babbi, A. Guglielmi, M. Airaldi (2022). Focal electroretinogram and microperimetry testing of photoreceptor-retinal pigment epithelium function in intermediate age-related macular degeneration. Acta Ophthalmologica, Vol. 100, 277-284


• D. Bystrova, G. Poggiato, B. Bektas, J. Arbel, J. S. Clark, A. Guglielmi, W. Thuiller (2021). Clustering species with residual covariance matrix in joint species distribution models. Frontiers in Ecology and Evolution, Vol. 9, 1-11


• V. Nicoletta, A. Guglielmi, A. Ruiz, V. Belangér, E. Lanzarone (2021). Bayesian spatio-temporal modelling and prediction of areal demands for ambulance services. IMA Journal of Management Mathematics, Vol. 33, 101-121


• M. Tallarita, M. De Iorio, A. Guglielmi, J. Malone-Lee (2020). Bayesian Autoregressive Frailty Models for Inference in Recurrent Events. The International Journal of Biostatistics, Vol. 16, 1-18


• G. Paulon, M. De Iorio, A. Guglielmi, F. Ieva (2020). Joint modelling of recurrent events and survival: a Bayesian nonparametric approach . Biostatistics, Vol. 21, 1-14


• I. Bianchini, A. Guglielmi, F.A. Quintana (2019). Determinantal point process mixtures via spectral density approach. Bayesian Analysis, , Vol. 15, 187-214


• A. Guglielmi, G. Guidoboni, A. Harris, I. Sartori and L. Torriani (2019). Statistical methods in medicine: application to the study of glaucoma. In Mathematical Modeling of Ocular Fluid Dynamics. From Theory to Clinical Applications (G. Guidoboni, A. Harris, R. Sacco Eds.), Birkhäuser, 599-612


• A. Guglielmi, F. Ieva, A.M. Paganoni, F.A. Quintana (2018). A semiparametric Bayesian joint model for multiple mixed-type outcomes: an Application to Acute Myocardial Infarction. Advances in Data Analysis and Classification, Vol. 12, 399-423


• V. Nicoletta, E. Lanzarone, A. Guglielmi, V. Belanger, A. Ruiz (2017). Bayesian models for describing and predicting the stochastic demand of emergency calls. In Bayesian Statistics in Action. BAYSM 2016. Springer Proceedings in Mathematics & Statistics, vol. 194, (Argiento R., Lanzarone E., Antoniano Villalobos I., Mattei A. Eds.), Springer, 203-212.


• Argiento, R., Guglielmi, A., Lanzarone, E., Nawajah, I. (2017). Bayesian joint modeling of the health profile and demand of home care patients. IMA Journal of Management Mathematics, Vol. 28, 531--552


• Argiento, R., Bianchini, I. Guglielmi, A. (2016). Posterior sampling from epsilon-approximation of normalized completely random measure mixtures. Electronic Journal of Statistics, Vol. 10, 3516--3547


• Argiento, R., Guglielmi, A., Lanzarone, E., Nawajah, I. (2016). A Bayesian framework for describing and predicting the stochastic demand of home care patients. Flexible Services and Manufacturing Journal, Vol. 28, 254--279. Post-print pdf


• Argiento, R., Bianchini, I. Guglielmi, A. (2016). A blocked Gibbs sampler for NGG-mixture models via a priori truncation. Statistics and Computing , Vol 26, 641--661.


• R. Argiento, A. Guglielmi, C.K. Hsiao, F. Ruggeri, C. Wang (2015). Modelling the association between clusters of SNPs and disease responses. In Nonparametric Bayesian Methods in Biostatistics and Bioinformatics ( R. Mitra, P. Mueller Eds.), Springer.


• Guglielmi, A., Ieva, F., Paganoni, A.M., Ruggeri, F., Soriano, J. (2014). Semiparametric Bayesian models for clustering and classification in presence of unbalanced in-hospital survival. Journal of the Royal Statistical Society, C (Applied Statistics), Vol.63, 25--46.


• Argiento, R., Cremaschi, A., Guglielmi (2014). A ``Density-Based'' Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models. Journal of Computational and Graphical Statistics, Vol. 23, 1126--1142. Post-print pdf


• Argiento, R., Guglielmi, A., Pievatolo, A. (2013). Estimation, prediction and interpretation of NGG random effects models. Statistical Papers, Vol. 55, 805--826.


• Guglielmi, A., Ieva, F., Paganoni, A.M., Ruggeri, F. (2013). Hospital clustering in the treatment of acute myocardial infarction patients via a Bayesian semiparametric approach. In Statistical Models for Data Analysis , eds. P. Giudici, S. Ingrassia, M. Vichi, Springer.


• Di Lucca, M.A., Guglielmi, A., Müller, P., Quintana, F.A. (2013). A simple class of Bayesian nonparametric autoregression models. Bayesian Analysis, Vol. 8, 63--88.


• Argiento, R., Guglielmi, A., Soriano, J. (2013). A semiparametric Bayesian generalized linear mixed model for the reliability of Kevlar fibres. Applied Stochastic Models in Business and Industry, Vol.29, 410--423.


• Guglielmi, A., Ieva, F., Paganoni, A.M., Ruggeri, F. (2012). A Bayesian random-effects model for survival probabilities after acute myocardial infarction. Chilean Journal of Statistics, Vol. 3, 15--29.


• Favaro, S., Guglielmi, A., Walker, S.A. (2012). A class of measure-valued Markov chains and Bayesian nonparametrics. Bernoulli, Vol. 18, 1002--1030.


• Guglielmi, A., Ieva, F., Paganoni, A.M., Ruggeri, F. (2012). Process indicators and outcome measures in the treatment of Acute Myocardial Infarction patients. In Statistical Methods in Healthcare (F. Faltin, R. Kenett and F. Ruggeri Eds.), Wiley.


• Giardina, F., Guglielmi, A., Quintana, F.A., Ruggeri, F. (2011). Bayesian first order autoregressive latent variable models for multiple binary sequences. Statistical Modelling, Vol. 11, 471--488.


• Argiento, R., Guglielmi, A., Pievatolo, A. (2010). Mixed-effects modelling of Kevlar fibre failure times through Bayesian nonparametrics. Complex data modeling and computationally intensive statistical methods, eds. P. Mantovan, P. Secchi, Springer, p. 13-26.


• Argiento, R., Guglielmi, A., Pievatolo, A. (2010). Bayesian density estimation and model selection using nonparametric hierarchical mixtures. Computational Statistics and Data Analysis, Vol. 54, 816--832.


• Argiento, R., Guglielmi, A., Pievatolo, A. (2009). A comparison of nonparametric priors in hierarchical mixture modelling for AFT regression. Journal of Statistical Planning and Inference, Vol. 139, 3989-4005.


• Epifani I., Guglielmi A., Melilli E. (2009). Moment-based approximations for the law of functionals of Dirichlet processes. Applied Mathematical Sciences, Vol. 3, no. 20, 979 - 1004.


• Betrò, B., Bodini, A., Guglielmi, A. (2006). Generalized moment theory and Bayesian robustness analysis for hierarchical mixture models . Annals of the Institute of Statistical Mathematics, vol. 58, 721-738.


• Epifani, I., Guglielmi, A., Melilli, E. (2006). A stochastic equation for the law of the random Dirichlet variance. Statistics & Probability Letters, vol. 76, 495-502.


• Regazzini, E., Guglielmi, A., Di Nunno, G. (2002). Theory and numerical analysis for exact distributions of functionals of a Dirichlet process. The Annals of Statistics, vol. 30, 1376-1411.


• Guglielmi, A., Holmes, C., Walker, S.G. (2002). Perfect simulation involving functionals of a Dirichlet process . Journal of Computational and Graphical Statistics, vol. 11, 306-310.


• Guglielmi, A., Tweedie, R.L. (2001). MCMC estimation of the law of the mean of a Dirichlet process. Bernoulli, vol. 7, 573-592.


• Berger, J.O., Guglielmi, A. (2001). Bayesian and conditional frequentist testing of a parametric model versus nonparametric alternatives. Journal of the American Statistical Association, vol. 96, 174 -184.


• Guglielmi, A., Melilli, E. (2000). Approximating de Finetti's measures for partially exchangeable sequences. Statistics & Probability Letters, vol. 48, 309-315.


• Betrò, B., Guglielmi A. (2000). Methods for global prior robustness under generalized moment conditions. In Robust Bayesian analysis, Lecture Notes in Statistics, v. 152, Eds. D. Rios Insua, F. Ruggeri, Springer, 273-294.


• Ruggeri, F., Barbieri, M.M., Guglielmi, A. (1999). Scelta del modello statistico . In Atti delle Giornate di Studio su ``Decisioni Statistiche'', Pitagora Editrice, Bologna, 116-138.


• Guglielmi, A. (1998). Risultati sulle distribuzioni di medie di un processo di Dirichlet. La matematica nella Società e nella Cultura, Bollettino U.M.I, vol.8, 1-A Suppl., 125-128.


• Guglielmi, A. (1998). A simple procedure calculating the generalized Stieltjes transform of the mean of a Dirichlet process. Statistics & Probability Letters, vol. 38, 299-303.


• Guglielmi, A., Melilli, E. (1998). Non-informative invariant priors yield peculiar marginals. Communications in Statistics - Theory and Methods, vol. 27, 2293-2306.


• Betrò, B., Guglielmi A. (1996). Numerical robust Bayesian analysis under generalized moments conditions . In Bayesian Robustness, IMS Lecture Notes, vol. 29, Eds. J. Berger, B. Betrò, E. Moreno, L. Pericchi, F. Ruggeri, G. Salinetti, L. Wasserman.

Technical reports and Proceedings

• M. Beraha, D. Falco, A. Guglielmi (2021). JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software. arXiv:2107.09357v1
• A. Guglielmi, M. Beraha, M. Gianella, M. Pegoraro, R. Peli (2021). A transdimensional MCMC sampler for spatially dependent mixture models. CLADAG 2021 - Book of abstracts and short papers, Eds: G. C. Porzio, C. Rampichini, C. Bocci, Firenze University Press, ISSN 2704-5846 (ONLINE)
• L. Ghilotti, M. Beraha, A. Guglielmi (2021). Anisotropic determinantal point processes and their application in Bayesian mixtures. Book of Short Papers SIS 2021, Eds: C. Perna, N. Salvati, F. Schirripa Spagnolo , Pearson Editor, ISBN: 9788891927361, p. 1226--1231.
• M. Gianella, M. Beraha, A. Guglielmi (2021). Spatially dependent mixture models with a random number of components. Book of Short Papers SIS 2021, Eds: C. Perna, N. Salvati, F. Schirripa Spagnolo , Pearson Editor, ISBN: 9788891927361, p. 936--941.
• M. Beraha, M. Pegoraro, R. Peli, A. Guglielmi (2020). A Bayesian model to induce dependence between mixtures. Book of Short Papers - SIS 2020, Pearson, Eds: A. Pollice, N. Salvati, F. Schirripa Spagnolo, ISBN: 9788891910776, p. 608--613.
• M. Beraha, G. Gualtieri, E. Villa, R. Vitali, A. Guglielmi (2020). Choosing the right tool for the job: a systematic analysis of general purpose MCMC software. Book of Short Papers - SIS 2020, Pearson, Eds: A. Pollice, N. Salvati, F. Schirripa Spagnolo, ISBN: 9788891910776, p. 661-666.
• M. Beraha, A. Guglielmi (2019). Invited discussion on ``Latent nested nonparametric priors'' by Camerlenghi F., Dunson D. B., Lijoi A., Prunster I. and Rodriguez A., Bayesian Analysis, Vol. 14, 1326-1332.
• M. De Iorio, S. Favaro, A. Guglielmi, L. Ye (2019). Bayesian nonparametric temporal dynamic clustering via autoregressive Dirichlet priors. arXiv:1910.10443
• A. Cadonna, A. Cremaschi, A. Guglielmi (2019). Bayesian modeling for large spatio-temporal data: an application to mobile networks. Book of Short Papers SIS 2019, Eds: G. Arbia, S. Peluso, A. Pini, G. Rivellini, Pearson Editor, ISBN: 9788891915108, p. 691-696.
• G. Bissoli, C. Principi, G.M. Rinaldi, M. Beraha, A. Guglielmi (2019). A Bayesian model for network flow data: an application to BikeMi trips. Book of Short Papers SIS 2019, Eds: G. Arbia, S. Peluso, A. Pini, G. Rivellini, Pearson Editor, ISBN: 9788891915108, p. 673-678.
• R. Argiento, I. Bianchini, A. Guglielmi, E. Lanzarone (2018). Bayesian nonparametric covariate driven clustering. Book of Short Papers SIS 2018, Eds: A. Abbruzzo, E. Brentari, D. Piacentino, M. Chiodi, Pearson Editor, ISBN: 9788891910233.
• G. Paulon, M. De Iorio, A. Guglielmi (2016). Bayesian autoregressive semiparametric models for gap times of recurrent events. SIS 2016, 48-th Scientific Meeting of the Italian Statistical Society, Proceedings, Eds: S. Cabras, T. Di Battista and W. Racugno, ISBN: 978-88-8467-874-4.
• I. Nawajah, R. Argiento, A. Guglielmi, E. Lanzarone (2014). Joint Prediction of Demand and Care Duration in Home Care Patients: a Bayesian Approach. SIS 2014, 47-th Scientific Meeting of the Italian Statistical Society, Proceedings, Eds: S. Cabras, T. Di Battista and W. Racugno, ISBN: 978-88-8467-874-4.
• R. Argiento, I. Bianchini, A. Guglielmi (2014). A Bayesian nonparametric model for density and cluster estimation: the $\varepsilon$-NGG process mixture. SIS 2014, 47-th Scientific Meeting of the Italian Statistical Society, Proceedings, Eds: Monica Pratesi and Cira Pena, ISBN: 9788861970618 .
• R. Argiento, A. Guglielmi (2014). Bayesian principal curve clustering by species-sampling mixture models. SIS 2014, 47-th Scientific Meeting of the Italian Statistical Society, Proceedings, Eds: S. Cabras, T. Di Battista and W. Racugno, ISBN: 978-88-8467-874-4.
• R. Argiento, A. Cremaschi, A. Guglielmi (2013). Cluster analysis of curved-shaped data with species-sampling mixture models. Proceedings of SCo2013 - Complex Data Modeling and Computationally Intensive Statistical Methods for Estimation and Prediction. Milano (ITALY), 9-11 September 2013.
• I. Nawajah, R. Argiento, A. Guglielmi, E. Lanzarone (2013). A Bayesian approach for modeling patient's demand and hidden health status: an application to Home Care. Proceedings of SCo2013 - Complex Data Modeling and Computationally Intensive Statistical Methods for Estimation and Prediction.
• A. Guglielmi, F. Ieva, A.M. Paganoni, E. Prandoni (2013). Joint modeling of multiple mixed-type outcomes using Bayesian semiparametrics: an application to acute myocardial infarction patients. Proceedings of SCo2013 - Complex Data Modeling and Computationally Intensive Statistical Methods for Estimation and Prediction.
• E. Prandoni, A. Guglielmi, F. Ieva, A.M. Paganoni (2013). A semiparametric Bayesian multivariate model for survival probabilities after acute myocardial infarction. In The contribution of young researchers to Bayesian statistics - Proceedings of BAYSM2013 (Springer Proceedings in Mathematics & Statistics, vol. 63), p. 1-5.
• R. Argiento, A. Guglielmi, F. Ieva, A. Parodi (2013). Analysis of hospitalizations of patients affected by chronic heart disease. In The contribution of young researchers to Bayesian statistics - Proceedings of BAYSM2013 (Springer Proceedings in Mathematics & Statistics, vol. 63), p. 1-5.
• R. Argiento, A. Guglielmi, E. Lanzarone, I. Nawajah (2013). Bayesian analysis and prediction of patients' demands for visits in home care. In The contribution of young researchers to Bayesian statistics - Proceedings of BAYSM2013 (Springer Proceedings in Mathematics & Statistics, vol. 63), p. 1-7.
• I. Nawajah, R. Argiento, A. Guglielmi, E. Lanzarone (2013). Estimating patient demand progression in home care: a Bayesian modeling approach. Proceedings of the 39th Conference on Operational Research Applied to Health Services (ORAHS 2013), p. 44-47.
• A. Cadonna, A. Guglielmi, F. A. Quintana (2011). Bayesian nonparametric AR(1)-models for multiple binary sequences. In 7th Conference on Statistical Computation and Complex Systems (SCo 2011), Conference Proceedings.
• A. Guglielmi, F. Ieva, A.M. Paganoni, F. Ruggeri, J. Soriano (2011). Semiparametric Bayesian approaches to mixed-effects models for outcome measures in the treatment of acute myocardial infarction. In 7th Conference on Statistical Computation and Complex Systems (SCo 2011), Conference Proceedings.
• A. Guglielmi, F. Ieva, A.M. Paganoni, F. Ruggeri, J. Soriano (2011). Hospital clustering in the treatment of acute myocardial infarction patients via a Bayesian nonparametric approach. Proceedings di Cladag 2011 (8th International Meeting of the Classification and Data Analysis Group), Pavia, 7-9 settembre 2011.
• A. Guglielmi, F. Ieva, A.M. Paganoni, F. Ruggeri (2010). A hierarchical random-effects model for survival in patients with Acute Myocardial Infarction. In Atti della XLV Riunione Scientifica della Società Italiana di Statistica 2010, Padova, 16-18 Giugno, 2010.
• R. Argiento, A. Guglielmi, A. Pievatolo (2007). Bayesian semiparametric inference for the AFT model, using N-IG mixture priors. In Atti del convegno intermedio 2007 ``Risk and Prediction'',6-8 giugno 2007, CLEUP, Padova.
• R. Argiento, A. Guglielmi, A. Pievatolo, F. Ruggeri (2006). Bayesian semiparametric inference for the accelerated failure time model using hierarchical mixture modeling with N-IG priors. Joint Statistical Meeting Proceedings, ASA Section on Bayesian Statistical Science, [CD-ROM].
• I. Epifani, A. Guglielmi and E. Melilli (2004). Some new results on random Dirichlet variance. IMATI-CNR TR 2004.15-MI.
• B. Betrò, A. Bodini, A. Guglielmi (2003). Un problema non parametrico di robustezza bayesiana nella classe dei momenti generalizzati. XVII Congresso UMI, Conferenze e Comunicazioni, 141-141.
• B. Betrò, A. Bodini, A. Guglielmi (2003). Nonparametric Robust Bayesian analysis under generalized moment conditions. Bulletin of the International Statistical Institute, 54-th Session, Contributed Papers, 443-444.
• J. O. Berger, A. Guglielmi (1999). Bayesian testing of a parametric model versus nonparametric alternatives. Proceedings of the Section on Bayesian Statistical Science of the American Statistical Association.
• A. Guglielmi (1998). Rate of convergence to the law of the mean of a Dirichlet process. Quaderno IAMI 98.18.
• A. Guglielmi and E.Melilli (1998). Measuring exchangeability in a partially exchangeable sequence. Quaderno IAMI 98.10.
• A. Guglielmi (1998). Numerical analysis for the distribution function of the mean of a Dirichlet process. Quaderno IAMI 98.1.

  A. Guglielmi (1997). Risultati sulle distribuzioni di medie di processi di Dirichlet . Ph.D. dissertation, Università degli Studi di Milano.

  B. Betrò, A. Guglielmi, F. Rossi (1996). Robust Bayesian analysis for the Power Law Process. Proceedings of the Section on Bayesian Statistical Science of the American Statistical Association.

  A. Guglielmi, E. Melilli (1996). Non-informative, invariant and improper priors: some consequences of their use. Atti della XXXVIII Riunione Scientifica della Società Italiana di Statistica.

  A. Guglielmi and E. Melilli (1994). A note on degenerate exchangeable sequences . Quaderno n. 45/1994, Dip. Matematica, Università degli Studi di Milano.