Scientific Articles

  • M. Beraha, B. Guindani, M. Gianella, A. Guglielmi (2024+).
    BayesMix: Bayesian Mixture Models in C++.
    Journal of Statistical Software, accepted for publication.
  • Ghilotti, L., Beraha, M., Guglielmi, A. (2024).
    Bayesian clustering of high-dimensional data via latent repulsive mixtures.
    Biometrika, Advance articles. DOI: 10.1093/biomet/asae059
  • van den Boom W., De Iorio M., Qian F., Guglielmi A. (2024).
    The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis.
    Biometrics, Vol. 80, ujae075. DOI: 10.1093/biomtc/ujae075
  • Guindani B., Ardagna D., Guglielmi A., Rocco R., Palermo G. (2024).
    Integrating Bayesian Optimization and Machine Learning for the Optimal Configuration of Cloud Systems.
    IEEE Transactions on Cloud Computing, Vol. 22, 277-294. DOI: 10.1109/TCC.2024.3361070
  • Argiento R., Corradin R., Guglielmi A., Lanzarone E. (2024).
    Clustering blood donors via mixtures of product partition models with covariates.
    Biometrics, Vol. 80, ujad021. DOI: 10.1093/biomtc/ujad021
  • Beraha M., Guglielmi A., Quintana F. A., de Iorio M., Eriksson J. G., Yap F. (2024).
    Childhood Obesity in Singapore: a Bayesian Nonparametric Approach.
    Statistical Modelling, Vol. 24, 541—560. DOI: 10.1177/1471082X231185892
  • Epifani I., Lanzarone E., Guglielmi A. (2023).
    Predicting donations and profiling donors in a blood collection center: a Bayesian approach.
    Flexible Services and Manufacturing Journal, Online First Articles. DOI: 10.1007/s10696-023-09516-8
  • 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. DOI: 10.1016/j.spasta.2022.100715
  • 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. DOI: 10.1080/10618600.2021.2000424
  • M. Beraha, M. Pegoraro, R. Peli, A. Guglielmi (2021).
    Spatially dependent mixture models via the Logistic Multivariate CAR prior.
    Spatial Statistics, Vol. 46, 100548. DOI: 10.1016/j.spasta.2021.100548
  • 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. DOI: 10.1214/21-BA1278
  • 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. DOI: 10.1111/aos.14934
  • 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. DOI: 10.3389/fevo.2021.601384
  • 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. DOI: 10.1093/imaman/dpaa028
  • 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. DOI: 10.1515/ijb-2018-0088
  • 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. DOI: 10.1093/biostatistics/kxy026
  • 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.
  • I. Bianchini, A. Guglielmi, F.A. Quintana (2019).
    Determinantal point process mixtures via spectral density approach.
    Bayesian Analysis, Vol. 15, 187-214. DOI: 10.1214/19-BA1150
  • 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. DOI: 10.1007/s11634-016-0273-7
  • 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. DOI: 10.1093/imaman/dpw001
  • Argiento, R., Bianchini, I. Guglielmi, A. (2016).
    Posterior sampling from ε-approximation of normalized completely random measure mixtures.
    Electronic Journal of Statistics, Vol. 10, 3516-3547. DOI: 10.1214/16-EJS1168
  • 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. DOI: 10.1007/s10696-014-9200-4
  • 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. DOI: 10.1007/s11222-015-9549-6
  • 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 Series C (Applied Statistics), Vol. 63, 25-46. DOI: 10.1111/rssc.12021
  • 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. DOI: 10.1080/10618600.2013.856796
  • Argiento, R., Guglielmi, A., Pievatolo, A. (2013).
    Estimation, prediction and interpretation of NGG random effects models: an application to Kevlar fibre failure times.
    Statistical Papers, Vol. 55, 805-826. DOI: 10.1007/s00362-013-0528-8
  • 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. DOI: 10.1214/13-BA803
  • 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. DOI: 10.1002/asmb.1936
  • 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. DOI: 10.3150/11-BEJ356
  • 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. DOI: 10.1177/1471082X1001100601
  • 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. DOI: 10.1016/j.csda.2009.11.002
  • 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. DOI: 10.1016/j.jspi.2009.05.004
  • Epifani I., Guglielmi A., Melilli E. (2009).
    Moment-based approximations for the law of functionals of Dirichlet processes.
    Applied Mathematical Sciences, Vol. 3, 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. DOI: 10.1007/s10463-006-0046-8
  • 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. DOI: 10.1016/j.spl.2005.08.036
  • 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. DOI: 10.1214/aos/1035844980
  • 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. DOI: 10.1198/106186002760180527
  • 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. DOI: 10.1198/016214501750333045
  • Guglielmi, A., Melilli, E. (2000).
    Approximating de Finetti's measures for partially exchangeable sequences.
    Statistics & Probability Letters, Vol. 48, 309-315. DOI: 10.1016/S0167-7152(00)00013-4
  • 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, 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. DOI: 10.1016/S0167-7152(98)00034-0
  • Guglielmi, A., Melilli, E. (1998).
    Non-informative invariant priors yield peculiar marginals.
    Communications in Statistics - Theory and Methods, Vol. 27, 2293-2306. DOI: 10.1080/03610929808832228

Technical Reports

  • M. Beraha, R. Argiento, F. Camerlenghi, A. Guglielmi (2024).
    Bayesian Mixtures Models with Repulsive and Attractive Atoms.
    arXiv:2302.09034 [math.ST]. DOI: 10.48550/arXiv.2302.09034
  • M. Gianella, M. Beraha and A. Guglielmi (2023).
    Bayesian nonparametric boundary detection for income areal data.
    arXiv:2312.13992 [stat.ME]. DOI: 10.48550/arXiv.2312.13992
  • A. Cremaschi, A. Cadonna, A. Guglielmi, F. A. Quintana (2023).
    A change-point random partition model for large spatio-temporal datasets.
    arXiv:2312.12396 [stat.ME]. DOI: 10.48550/arXiv.2312.12396
  • I. Epifani, A. Guglielmi and E. Melilli (2004).
    Some new results on random Dirichlet variance.
    IMATI-CNR TR 2004.15-MI.
  • 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.
  • A. Guglielmi and E. Melilli (1994).
    A note on degenerate exchangeable sequences.
    Quaderno n. 45/1994, Dip. Matematica, Università degli Studi di Milano.

Papers in Conference Proceedings

  • L. Barone, B. Guindani, R. Sala, Gadioli, D. Ardagna, A. Guglielmi (2024).
    PAK-MAN: Enhancing Parallel Bayesian Optimization of Cloud and HPC Systems via Machine Learning.
    Accepted for publication in EAI VALUETOOLS 2024.
  • R. Sala, B. Guindani, D. Ardagna, A. Guglielmi (2024).
    d-MALIBOO: a Bayesian Optimization framework for dealing with Discrete Variables.
    Proceedings of the 32nd International Conference on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), p. 1-8. DOI: 10.1109/MASCOTS64422.2024.10786339.
  • M. Frigeri, L. Marchesin, M. Coppellotti, A. Guglielmi (2024).
    A Bayesian binomial regression model for ozone levels in Northern Italy.
    Methodological and Applied Statistics and Demography IV, Eds: A. Pollice, P. Mariani, Springer Editor, ISBN: 9783031644498, p. 40-45. DOI: 10.1007/978-3-031-64447-4_7.
  • F. Wolf, A. Carminati, A. Guglielmi (2024).
    Spatio-temporal clustering of PM2.5 in northern Italy using a Bayesian model.
    Methodological and Applied Statistics and Demography I, Eds: A. Pollice, P. Mariani, Springer Editor, ISBN: 9783031643484, p. 218-223. DOI: 10.1007/978-3-031-64346-0_37.
  • M. Gianella, A. Guglielmi (2024).
    Model-based clustering of spatial time series through the BayesMix library.
    Methodological and Applied Statistics and Demography IV, Eds: A. Pollice, P. Mariani, Springer Editor, ISBN: 9783031644498, p. 102-107. DOI: 10.1007/978-3-031-64447-4_17.
  • M. Frigeri, A. Guglielmi, G. Lonati (2023).
    A Bayesian weather-driven spatio-temporal model for PM10 in Lombardy.
    Book of Short Papers - SIS 2023 (SEAS IN), Eds: F. M. Chelli, M. Ciommi, S. Ingrassia, F. Mariani, M. C. Recchioni, Pearson Editor, ISBN: 9788891935618, p. 1009-1014.
  • B. Guindani, D. Ardagna, A. Guglielmi (2022).
    Bayesian Optimization with Machine Learning for big data applications in the cloud.
    Book of Short Papers - SIS 2022, Eds: A. Balzanella, M. Bini, C. Cavicchia, R. Verde, Pearson Editor, ISBN: 9788891932310, p. 1479-1484
  • M. Gianella, A. Guglielmi, G. Lonati (2022).
    A Bayesian spatio-temporal model of PM10 pollutant in the Po Valley.
    Book of Short Papers - SIS 2022, Eds: A. Balzanella, M. Bini, C. Cavicchia, R. Verde, Pearson Editor, ISBN: 9788891932310, p. 883-888.
  • L. Ghilotti, M. Beraha, A. Guglielmi (2022).
    Repulsive mixture models for high-dimensional data.
    Book of Short Papers - SIS 2022, Eds: A. Balzanella, M. Bini, C. Cavicchia, R. Verde, Pearson Editor, ISBN: 9788891932310, p. 32-36.
  • B. Guindani, D. Ardagna, A. Guglielmi (2022).
    MALIBOO: when Machine Learning meets Bayesian Optimization.
    Proceedings of the 7th IEEE International Conference on Smart Cloud (IEEE SmartCloud 2022), p. 1-9. DOI: 10.1109/SmartCloud55982.2022.00008.
  • 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, Pearson Editor, 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, Pearson Editor, ISBN: 9788891910776, p. 661-666.
  • 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: 9788884678744.
  • 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: 9788884678744.
  • 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: 9788884678744.
  • 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. Milano (ITALY), 9-11 September 2013.
  • 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. Milano (ITALY), 9-11 September 2013.
  • 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.
  • 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, p. 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.
  • 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.

Contributions to Books

  • 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.
  • 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.
  • 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. (2013).
    Hospital clustering in the treatment of acute myocardial infarction patients via a Bayesian semiparametric approach.
    In: Statistical Models for Data Analysis (Giudici P., Ingrassia S., Vichi M. Eds.), Springer.
  • 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 (Faltin F., Kenett R., Ruggeri F. Eds.), Wiley.
  • Argiento, R., Guglielmi, A., Pievatolo, A. (2010).
    Mixed-effects modelling of Kevlar fibre failure times through Bayesian nonparametrics.
    In: Complex data modeling and computationally intensive statistical methods (Mantovan P., Secchi P. Eds.), Springer, 13-26.
  • Betrò, B., Guglielmi A. (2000).
    Methods for global prior robustness under generalized moment conditions.
    In: Robust Bayesian analysis, Lecture Notes in Statistics, Vol. 152 (Rios Insua D., Ruggeri F. Eds.), 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, 116-138.
  • Betrò, B., Guglielmi A. (1996).
    Numerical robust Bayesian analysis under generalized moments conditions.
    In: Bayesian Robustness, IMS Lecture Notes, Vol. 29 (Berger J., Betrò B., Moreno E., Pericchi L., Ruggeri F., Salinetti G., Wasserman L. Eds.).