Raffaele Argiento

foto_sito Ritratto di zio Raffaele by Anita

 

About me

I'm Full Professor of Statistics at the Department of Economics -- Università degli Studi di Bergamo.

I work in the areas of Bayesian Statistics, both parametric and nonparametric. My methodological work focuses mainly on Bayesian mixture models and associated computational strategies


Services


Other things about me


Past Affiliations

Research

My methodological work focuses mainly on Bayesian nonparametric mixture models and associated computational strategies. In particular, I consider setups where the mixing distribution is a normalized measure. I deeply investigate the link between infinite and finite mixture models. In particular, for the finite dimensional case, I have introduced new computational methods allowing for automatic transdimentional moves and overcoming many of the challenges associated with the Reversible Jump Markov chain Monte Carlo. A main area of application I have considered for mixture models is clustering, focusing on data which are grouped into clusters with non-standard geometrically shapes, and more recently, on covariate driven clustering models. Moreover, I have worked on applied problems arising in Biology, Sport Analytics and Ecology.

Recent projects

  • 2022 Principal investigator - CluB-PMx² – CLUstering: Bayesian Partition Models for Precise Medicine - Funded by
    Fondo di Beneficienza di Intesa San Paolo – Joint project between Collegio Carlo Alberto, University of Bergamo and University of Firenze.
  • 2018-19 Principal investigator - University of Torino, ESOMAS Department research project Dependent models in Bayesian Nonparametric Statistics: computational aspects.
  • 2017-18 Principal investigator – University of Torino, ESOMAS Department research project Methodological and computational problems in Modern Bayesian nonparametric inference.
  • 2016-2017 Individual project – University of Torino, Young Excellence ESOMAS Department research project Bayesian nonparametric cluster analysis, new challanges in the “big data” era.
  • 2013-15, Co-investigator - Framework agreement between Regione Lombardia and CNR: INTEGRATE. Innovazioni Tecnologiche per una Gestione Razionale del Tessuto Edilizio. (Technologial innovations for a rational management of the built environment.)
  • 2014: Co-investigator - Fab@Hospital, a Flagship project, CNR and MIUR (Ministero dell'Istruzione, dell'Università e della Ricerca). Factory of the Future: Hospital factory for manufacturing customized, patient specific 3D anatomo-functional model and prostheses .

Publications


Peer-Reviewed Journals


  1.  Argiento, R., Corradin, R., Guglielmi, A, Lanzarone  (2023+) "Clustering blood donors via mixtures of product partition models with covariates", Biometrics, Just accepted

  2. Pedone, M., Argiento, R., Stingo, F.C. (2023+) "Personalized Treatment Selection via Product Partition Models with Covariates", Biometrics, Just accepted

  3. Colombi, A., Argiento, R., Paci, L. and Pini, A. (2023+). "Learning Block Structured Graphs in Gaussian Graphical Models, Journal of Computational and Graphical Statistics, Just Accepted.
    doi: 10.1080/10618600.2023.2210184

  4.  Argiento, R., De Iorio, M. (2022 +). "Is infinity that far? A  Bayesian nonparametric perspective of finite mixture models". The Annals of Statistics.  volume 50,  issue 5, pp 2641-2663
    doi:    10.1214/22-AOS2201

  5. Matechou, E., Argiento, R. (2022+). "Capture-recapture models with heterogeneous temporary emigration". Journal of American Statistical Association. Latest Article (on-line),
    doi.org/10.1080/01621459.2022.2123332

  7. Cremaschi, A., Argiento, R., De Iorio, M., Shirong, C. Chong, Y.C., Meaney M.J., Kee, M.Z.L (2022+). "Seemingly Unrelated Multi-State processes: a Bayesian semiparametric approach". Bayesian Analysis. Advance Publication 1-23 (2022).
    doi: 10.1214/22-BA1326

  8. Dolmeta, P., Argiento, R., Montagna, S. (2022+). "Bayesian GARCH Modeling of Functional Sports Data". Statistical Methods & Applications. (available on-line),
    doi:     10.1007/s10260-022-00656-z

  9. Berloco, C., Argiento, R., Montagna, S. (2022+). "Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods". International Journal of Forecasting. In Press (available on line),
    doi.org/10.1016/j.ijforecast.2022.05.003.

  10. Codazzi L., Colombi A., Gianella M., Argiento R., Paci L., Pini A. (2022) "Gaussian graphical modeling for spectrometric data analysis", Computational Statistics & Data Analysis. volume 174  pp 107416
    doi: 10.1016/j.csda.2021.107416.

  11. Beraha, M., Argiento, R., Møller, J., Guglielmi, A. (2022). "MCMC computations for Bayesian mixture models using repulsive point processes". Journal of Computational and Graphical Statistics, volume 31, issue 2, pp  422-435,
    doi:10.1080/10618600.2021.2000424. Preprint available.

  12.  Lopes de Oliveira, G., Argiento, R., Loschi, R.H.,  Assunção R.M. , Ruggeri, F., D’Elia Branco, M. (2022) " Bias correction in clustered underreported data",   Bayesian Analysis, volume 17,  issue 1, pp 95-126, doi:10.1214/20-BA1244

  13. Montagna, S, Orani, V., Argiento, R. (2021) "Bayesian isotonic logistic regression via constrained splines: an application to estimating the serve advantage in professional tennis", Statistical Methods & Applications, volume 30 , pp 573-604  doi: 10.1007/s10260-020-00535-5. Preprint available.

  14. Argiento, R., Cremaschi, A. and Vannucci, M. (2020) “Hierarchical Normalized Completely Random Measures to Cluster Grouped Data”, Journal of the American Statistical Association, volume 115 issue 529, pp 318-333.  doi:10.1080/01621459.2019.1594833 Preprint availble

  15. Cremaschi, A., Argiento, R., Shoemaker, K., Peterson, C.B. and Vannucci M. (2019) "Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling", Bayesian Analysis. volume 14, issue 4, pp. 1271-1301. doi:10.1214/19-BA1153  Preprint available.

  16. Argiento R., Ruggiero, M. (2018) “Computational challenges and temporal dependence in Bayesian nonparametric models”, Statistical Methods and Applications, volume 27, pp 231-238.
    doi: 10.1007/s10260-017-0397-8

  17.  Wadsworth W.D., Argiento R., Guindani M., Galloway-Pena J., Shelbourne S.A., Vannucci M. (2017)
    “An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abun ndances in microbiome data”, BMC Bioinformatics, Volume 18, pp 1-12. doi: 10.1186/s12859-017-1516-0

  18.  Wang, C., Hsiao, K., Ruggeri, F., Argiento, R. (2017) “Bayesian Nonparametric Clustering and Association Studies for Candidate SNP Observations”, International Journal of Approximate Reasoning. Volume 80, pp 19-35,
    doi:10.1016/j.ijar.2016.07.014

  19. Argiento, R., Guglielmi, A., Lanzarone, E., Nawajah, I. (2017) ``Joint prediction of health status and demand for patient in home care services: a Bayesian approach'', IMA Journal of Management Mathematics, volume 28, issue 4,  pp 531–552, doi: doi.org/10.1093/imaman/dpw001

  20. Argiento, R., Bianchini, I. Guglielmi, A. (2016) ``Posterior sampling from epsilon-approximation of normalized completely random measure mixtures'', Electronic Journal of Statistics, volume 10, issue 2, pp 3516-3547
    DOI:10.1214/16-EJS1168,

  21. Argiento, R., Bianchini, I. Guglielmi, A. (2016) ``A blocked Gibbs sampler for NGG-mixture models via a priori truncation'' Statistics and Computing, volume 26, issue 3, pp 641-666  , DOI: 10.1007/s11222-015-9549-6

  22. Argiento R., Guglielmi A., Lanzarone E., Nawajah I. (2014) ``A Bayesian framework for describing and predicting the stochastic demand of home care patients'', Flexible Services and Manufacturing Journal, volume 28, issue 1–2, pp 254–279 ; doi:10.1007/s10696-014-9200-4

  23. Bianchini, I., Argiento, R., Auricchio, F., Lanzarone E. (2015) ``E'', Computational Mechanics, volume 56, issue 3, pp 533–549 . DOI: 10.1007/s00466-015-1185-7

  24. Argiento, R., Bissiri, P. G., Pievatolo, A., Scrosati, C. (2015) ``Multilevel functional principal component analysis of faćade sound insulation data'', Quality and Reliability Engineering International, volume 31, issue 7, pp 1239–1253 , DOI: 10.1002/qre.184

  25. 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, volume 23 issue 4, pp 1126-1142. doi: 10.1080/10618600.2013.856796

  26. Argiento, R., Guglielmi, A., Pievatolo, A. (2014) ``Estimation, prediction and interpretation of NGG random effects models: an application to Kevlar fibre failure times'', Statistical Papers, volume 55, issue 3, pp 805-826 doi: 10.1007/s00362-013-0528-8

  27. Argiento, R., Guglielmi, A., Soriano, J. (2013) ``A semiparametric Bayesian generalized linear mixed model for the reliability of Kevlar fibers'', Applied Stochastic Models in Business and Industry, volume 29, issue 5, pp 410-423. doi: 10.1002/asmb.1936

  28. Argiento, R., Faranda, R., Pievatolo A., Tironi, E. (2012) ``Distributed Interruptible Load Shedding and Micro-Generator Dispatching to Benefit System Operations'', IEEE Transactions on Power Systems, volume 27, issue 2, pp. 840-848. doi: 10.1109/TPWRS.2011.2173217

  29. Argiento, R., Guglielmi, A., Pievatolo, A. (2010) ``Bayesian density estimation and model selection using nonparametric hierarchical mixtures',' Computational Statistics & Data Analysis, volume 54, issue 4, pp. 816-832. doi:10.1016/j.csda.2009.11.002

  30. Argiento, R., Guglielmi, A., Pievatolo, A. (2009) ``A comparison of nonparametric priors in hierarchical mixture modelling of lifetime data'', Journal of Statistical Planning and Inference, volume 139, issue 12, pp. 3989-4005. doi:10.1016/j.jspi.2009.05.004

  31. Argiento, R., Pemantle, R., Skyrms, B., Volkov, S. (2009) ``Learning to signal: analysis of a micro-level reinforcement model'', Stochastic Processes and their Applications, volume 119, issue 2, pp. 373-390. doi:10.1016/j.spa.2008.02.014

  32. Pievatolo, A., Ruggeri, F., Argiento R. (2003) ``Bayesian analysis and prediction of failures in underground trains'', Quality and Reliability Engineering International,  volume 19, issue 4, pp. 327-336. doi: 10.1002/qre.583

Book Chapters and Edited Books

  1.  Ghidini, V.,  Legramanti, S.,  Argiento, R. (2023), Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks. Bayesian Statistics, New Generations New Approaches (BAYSM2022), to appear.

  2.  Argiento, R., Camerlenghi, F., & Paganin, S. (Edited by). (2022). New Frontiers in Bayesian Statistics: BAYSM 2021}. Springer Proceedings in Mathematics & Statistics. Springer Interantional Publishing, doi: 10.1007/978-3-031-16427-9

  3. Pedone, M., Argiento, R., & Stingo, F. C. (2022). Bayesian Nonparametric Predictive Modeling for Personalized Treatment Selection. In New Frontiers in Bayesian Statistics: BAYSM 2021, (pp. 101-109). Springer Proceedings in Mathematics & Statistics. Springer Interantional Publishing, doi: 10.1007/978-3-031-16427-9.

  4. Argiento R., Durante D., Wade, S. (Edited by) (2019). \emph{Bayesian Statistics: New Challenges and New Generations - BAYSM 2018}. Springer Proceedings in Mathematics & Statistics. Springer Interantional Publishing. doi: 10.1007/978-3-030-30611-3

  5. Argiento R., Lanzarone E., Villalobos Antoniano I., Mattei A. (Edited by) (2017). Bayesian statistics in action - Proceedings of BAYSM 2016. , Springer Proceedings in Mathematics & Statistics. Springer Interantional Publishing.  doi: 10.1007/978-3-319-54084-9

  6. Argiento, R. (2016) "Credible Interval", in Wiley StatsRef: Statistics Reference Online,
    doi: 10.1002/9781118445112.stat07830

  7. Argiento, R., Guglielmi, A., Hsiao, C.K., Ruggeri, F., Wang C. (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. ISBN: 978-3-319-19517-9

  8. Argiento, R., Pemantle, R., Skyrms, B., Volkov, S. (2014) ``Learning to signal: analysis of a micro-level reinforcement model'' Reprint with the kind permission of the original publisher in Social Dynamics, Brian Skryrms (eds). Oxford University Press, United kingdom. ISBN: 978-0-19-965282-2 (hbk.)

  9. 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 Physica Verlag (Germania, Heidelberg) pp 13-26. ISBN: 978-88-470-1385-8

  10. Ruggeri, F., Pievatolo, A., Argiento, R. (2003) ``On a Bayesian model for failure prediction in underground trains''. In: Safety & Reliability. vol. 2, Maastricht (NL), pp. 1345-1349, ISBN 9058095517.

Proceedings

  1. Ghidini, V., Argiento, R., Legramanti, S. (2023) "Binomial Extended Stochastic Block Model for Brain Networks". In Chelli, F.M., Ciommi M,, Ingrassia, S., Mariani, F., Recchioni M.C. (Eds) . Book of short papers SIS 2023, pp 1121-1126. Published by Pearson.  ISBN 978889193561

  2. Colombi, A., Argiento, R., Camerlenghi, F., Paci, L. (2023) "Finite Mixture Model for Multiple Sample Data". In Chelli, F.M., Ciommi M,, Ingrassia, S., Mariani, F., Recchioni M.C. (Eds) . Book of short papers SIS 2023, pp 913-917. Published by Pearson.  ISBN 9788891935618

  3. Dolmeta, P., Argiento, R. and Montagna, S. (2022)  "Bayesian functional mixed effects model for sports data". In Balzanella, A., Bini, M., Cavicchia, C. and Verde, R. (Eds.),  Book of short papers SIS 2022, pp 1473-1478.  Published by Pearson.  ISBN 9788891932310

  4. Costa Fontichiari P., Giuliani M., Argiento R., Paci L., (2021) "Group-dependent finite mixture model". In Porzio G. C., Rampichini C., Bocci C. (Eds), CLADAG 2021 Book of abstracts and short papers, Firenze University Press, pp 304-307. ISBN 978-88-5518-340-6

  5. Filippi-Mazzola E., Argiento R., Paci L., (2021) "Clustering categorical data via Hamming distance". In Perna C., Salvati N., Schirripa Spagnolo F. (Eds), Book of short papers SIS 2021, Pearson, pp 752-757 ISBN 978-88-5518-340-6

  6. Raffaele Argiento, Bruno Bodin and Maria De Iorio Bayesian (2020) "Mixture Models for Latent Class Analysis". In Pollice A, Salvati N., Schirripa S (Eds), Book of short papers SIS 2020, Pearson, pp 429 - 434, ISBN 9788891910776

  7. Codazzi L., Colombi A., Gianella M., Argiento R., Paci L., Pini A., (2020) "Functional Graphical Model for Spectrometric Data Analysis". In Pollice A, Salvati N., Schirripa S (Eds), Book of short papers SIS 2020, Pearson, pp 852 - 856, ISBN 9788891910776

  8. Argiento, R. (2016) "A conditional algorithm for Bayesian finite mixture models via normalized point process''.Proceedings of 48th SIS Scientific Meeting of the Italian Statistica Society Salerno, June 8-10, 2016. ISBN: 978-88-6197-061-8

  9. Argiento, R., Guglielmi, A., Hsiao, C.K., Ruggeri, F., Wang C. (2016) ``A Baysian nonparametric    Approach to Model Association between Clusters of SNPs and Disease Responses''. Book of  abstracts of CLADAG 2015, 10th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, October 8-10, 2015. ISBN: 978-88-84-67-949-9

  10. R. Argiento, I. Bianchini, A. Guglielmi (2014) ``A Bayesian nonparametric model for density and cluster estimation: the ε-NGG process mixture''. Proceedings of 47th SIS Scientific Meeting of the Italian Statistica Society Cagliari, June 10-14, 2014. ISBN: 978-88-8467-874-4

  11. R. Argiento, A. Guglielmi (2014) ``Bayesian principal curve clustering by species-sampling mixture models'' Proceedings of 47th SIS Scientific Meeting of the Italian Statistica Society Cagliari, June 10-14, 2014. ISBN: 978-88-8467-874-4

  12. I. Nawajah, R. Argiento, A. Guglielmi, E. Lanzarone (2014) ``Joint Prediction of Demand and Care Duration in Home Care Patients: a Bayesian Approach'' Proceedings of 47th SIS Scientific Meeting of the Italian Statistica Society Cagliari, June 10-14, 2014. ISBN: 978-88-8467-874-4

  13. 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.; ISBN 978-3-319-02083-9

  14. 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. ISBN 978-3-319-02083-9

  15. 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. ISBN 978-605-64131-0-0.

  16. 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. ISBN: 9788864930190

  17. 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. ISBN:9788864930190

  18. Argiento, R., Guglielmi, A., Pievatolo, A. (2009) ``A semiparametric Bayesian Mixed-effects Model for Failure Time Data''. Proceedings of SCo209 - Complex Data Modeling and Computationally intensive Statistical Methods for Estimation and Prediction. ISBN: 9788838743851, Milano. pg 17-22.

  19. Argiento, R., Pievatolo, A., Ruggeri, F., Guglielmi, A. (2007) ``Bayesian semiparametric inference for the AFT model, using N-IG mixture prior''. In: Rischio e Previsione (Risk and Prediction). ISBN: 9788861290938, Venezia, p. 569-570.


  20. Argiento, R., Guglielmi, A., Pievatolo, A., Ruggeri, F. (2006) ``Bayesian semiparametric inference for the accelerated failure time model using hierarchical mixture modeling with N-IG priors'', 2006 Proceedings of the American Statistical Association, Seattle (USA).

  21. Argiento, R., Cagno, E., Caron, F., Mancini, M., Pievatolo, A., Ruggeri, F. (2002) ``Seasonal patterns and double measurement scale in modelling failures in underground trains'', MMR'2002 - 3rd International Conference on Mathematical Methods in Reliability (H. Langseth and B. Lindqvist, Eds.), pag. 45-48, NTNU.

Teaching

Contact

  • e-mail: raffaele dot argiento at unibg dot it