Raffaele Argiento

Ritratto di zio Raffaele by Anita

About me

I'm assistant professor of Statistics at University of Torino. I work in the areas of Bayesian statistics, both parametric and nonparametric see below for more information.


Affiliations

Other things about me

  • Board of BaYSM (Bayesian young statisticians meeting); The next edition BAYSM 2020 will be held the 26 and 27 of June in Kunming -- Yunnan (China) -- in the same location but right before the ISBA 2020 world meeting.
  • Executive director of ABS, Applied Bayesian Summer Scool; Give a look at the last edition.
  • Spring term 2019, visiting YaleNUS College in Singapore.
  • In the academic year 2015-16, I have been Lecturer in Statistics at University of Kent, School of Mathematics, Statistics and Actuarial Sciences, Canterbury (UK).
  • My home town is Sessa Aurunca a small and beautiful city on the north of Campania region.

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 completely random measure (e.g. the Dirichlet process or the normalized generalized gamma process), and I have introduced novel computational methods for this type of problems that belong to the so called class of conditional algorithms based on truncation. A main area of application I have considered for nonparametric 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 (e.g. SNP's data), health care systems (e.g. home care providers) and Engineering (e.g. sound measurements data).

Recent projects

  • 2018-19 Principal investigator - University of Torino, ESOMAS Department research project Dependent models in Bayesian Nonparametric Statistics: computational aspects.
  • 2017-18 Principal investigatorUniversity of Torino, ESOMAS Department research project Methodological and computational problems in Modern Bayesian nonparametric inference.
  • 2016-2017 Individual projectUniversity 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., Cremaschi, A. and Vannucci, M. (2019). “Hierarchical Normalized Completely Random Measures to Cluster Grouped Data”, Journal of the American Statistical Association. Just accepted. Preprint availble

  2. Cremaschi, A., Argiento, R., Shoemaker, K., Peterson, C.B. and Vannucci M. (2019). "Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling". Bayesian Analysis.  Just accepted Preprint available.

  3.  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

  4.  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

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

  6. 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

  7. 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

  8. 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

  9. 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  Online Firs; doi:10.1007/s10696-014-9200-4

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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 (2009) Volume 139, Issue 12, pp. 3989-4005. doi:10.1016/j.jspi.2009.05.004

  18. 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

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

Book Chapters and Edited Books

  1. Argiento R., Lanzarone E., Villalobos Antoniano I., Mattei A. (Edited by) (2017). Bayesian statistics in action - Proceedings of BAYSM 2016. p. 1-251, Cham (ZG):Springer, doi: 10.1007/978-3-319-54084-9

  2. Argiento, R. (2016) ``Credible Interval'', just accepted for pubblication in Wiley StatsRef: Statistics Reference Online, doi: 10.1002/9781118445112.stat07830

  3. 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

  4. 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.)

  5. 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

  6. 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. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. 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.


  13. 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).

  14. 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.

 

Preprints

  1. Argiento, A. , Oliveira G.L., Loschi. R. H., Branco, M., Ruggeri, F. (2019)  “Modeling underreported counts using auxiliary clustering variables”. Submitted.

  2.  Mathechou, E., Argiento, R. “Capture-recapture models with temporary emigration: a Bayesian non-parametric changepoint process approach”. to be submitted.

  3.  Argiento, R., Bianchini, I., Guglielmi, A. “Bayesian nonparametric covariate-driven clustering: An application to blood donors data” to be submitted.

  4. Argiento, R., De Iorio, M. “Is infinity that far?  A  Bayesian nonparametric perspective of finite mixture models”. To be submitted.

  5. Argiento, R., Bianchini, I., Griffin, J. “Exploiting conjugacy to build time dependent completely random
    measures
    ” In preparation.

Teaching

My teaching experience is quite broad, and my area of expertise is in computational modules. I taught undergraduate, graduate and Ph.D courses for a vast range of programs among the other Economics, Mathematical Engineering and Statistics.

I'm executive director of the Applied Bayesian Statistics School ABS; click here to visit ABS 16 web page and here for a list of past ABS schools.

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