International Journal of Probability and Statistics

International Journal of Probability and Statistics aims to publish refereed, well-written original research articles, and studies that describe the latest research and developments in the area of probability and statistics, and to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in the area of Probability and Statistics.

Massimo Aria

Editorial Board Member of International Journal of Probability and Statistics

Assistant Professor, Department of Mathematics and Statistics, University of Naples Federico II, Italy

Research Areas

COMPUTATIONAL AND APPLIED STATISTICS, PARAMETRIC AND NON PARAMETRIC CLASSIFICATION AND REGRESSION MODELS, STATISTICS FOR SOCIAL SCIENCES, STATISTICAL SURVEYS

Publications: Journals

[1]  Fortuna G., Lozada-Nur F., Chainani-Wu N., Aria M., Cepeda-Valdes R., Pollio A., Marinkovich M.P., Martinez-Salazar A.E., Mignogna M., Bruckner A.L., Salas-Alanís J.C. (2011) Epidermolysis bullosa Oropharyngeal Severity score (EBOS): a multicentric development and validation submitted to British Journal of Dermatology, Wiley-Blackwell.
[2]  D’Ambrosio, A., Aria, M., Siciliano, R. (2011) Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm, in press on Journal of Classification.
[3]  Mignogna M., Fortuna G., Pollio A., Aria M., Adamo D., Leuci S., Ruoppo E., (2011) Multiple myeloma versus breast cancer patients with bisphosphonates-related osteonecrosis of the jaws: a comparative analysis of response to treatment and predictors of outcome, Journal of Oral Pathology and Medicine, Wiley-Blackwell, DOI: 10.1111/j.1600-0714.2011.01095.x, ISSN: 1600-0714.
[4]  Montella A., Aria M., D’Ambrosio A., Mauriello F. (2011). Analysis of Powered Two-wheeler crashes in Italy by classification trees and rules discovery, in Accident Analysis & Prevention, Springer Elsevier, DOI: 10.1016/j.aap.2011.04.025.
[5]  Montella A., Aria M., D’Ambrosio A., Mauriello F. (2011). Data Mining Techniques for Exploratory Analysis of Pedestrian Crashes, Transportation Research Record – Journal of Transportation Research Board, accettato per la pubblicazione.
[6]  Montella A., Aria M., D’Ambrosio A., Galante F., Mauriello F., Pernetti M. (2011). Simulator evaluation of drivers' speed, deceleration and lateral position at rural intersections in relation to different perceptual cues, in Accident Analysis & Prevention, Springer Elsevier, DOI: 10.1016/j.aap.2011.05.030.
[7]  Montella A., Aria M., D’Ambrosio A., Galante F., Mauriello F., Pernetti, M. (2010). Perceptual Measures to Influence Operating Speeds and Reduce Crashes at Rural Intersections, Transportation Research Record – Journal of Transportation Research Board, vol. 2149, pp. 11-20, ISBN 9780309142809.
[8]  Galante F., Mauriello, F., Montella A., Pernetti M., Aria, M., D’Ambrosio A. (2010). Traffic Calming Along Rural Highways Crossing Small Urban Communities: a Driving Simulator Experiment in Accident Analysis & Prevention, Springer Elsevier, doi:10.1016/j.aap.2010.03.017.
[9]  Aria, M. (2009). Parallel Networks for Compositional Longitudinal Data, in Italian Journal of Applied Statistics, Volume 20, N.1, RCE multimedia, ISSN 1125-1964, pag. 5-20.
[10]  Aria, M., Gallo, S., Murolo, F., Siciliano, R. (2008). Le leve discriminanti della soddisfazione: il caso SEPSA, in Rivista di Economia e Statistica del Territorio, Fondazione Istituto Tagliacarne, Roma, ISSN 1971-0380, N.1 gennaio-aprile 2008.

Publications: Books/Book Chapters

[1]  Giordano G., Aria M. (2011). Regression trees with moderating effects. New Perspectives in Statistical Modeling and Data Analysis, a cura di Ingrassia S., Rocci R., Vichi M., in Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, ISBN: 978-3-642-11362-8.
[2]  Siciliano, R., Aria, M.. (2011). TWO-CLASS Trees for Non-Parametric Regression Analysis. Classification and Multivariate Analysis for Complex Data Structures, a cura di Fichet B., Piccolo D., Verde R. e Vichi M. in Series of Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, ISBN: 978-3-642-13311-4.
[3]  Siciliano, R., Aria, M., D’Ambrosio A. (2008). Posterior Prediction Modelling of Optimal Trees, Relazione Invitata in Proceedings in Computational Statistics: 18th Symposium of IASC Held in Porto, August 2008 (COMPSTAT2008), Physica-Verlag, ISBN 978-3-7908-2083-6, pp.322-334.
[4]  D’Ambrosio, A., Aria, M., Siciliano, R. (2007). Robust Tree-based Incremental Imputation Method for Data Fusion, in the Lecture Notes in Computer Science Series of Springer, Proceedings of the 7th International Symposium on Intelligent Data Analysis (IDA 2007) Berthold, Michael R.; Shawe-Taylor, John; Lavrac, Nada (Eds.) 2007, XIV, 380 p., Softcover ISBN: 978-3-540-74824-3, Springer, 6-8 settembre Ljubliana.
[5]  Tutore V.A., Siciliano, R., Aria, M. (2007). Conditional Classification Trees using Instrumental Variables, in the Lecture Notes in Computer Science Series of Springer, Proceedings of the 7th International Symposium on Intelligent Data Analysis (IDA 2007) Berthold, Michael R.; Shawe-Taylor, John; Lavrac, Nada (Eds.) 2007, XIV, 380 p., Softcover ISBN: 978-3-540-74824-3, Springer, 6-8 settembre Ljubliana.
[6]  Siciliano, R., Aria, M., D’Ambrosio, A. (2006). Boosted Incremental Tree-based Imputation of Missing Data, In Studies in Classification, Data Analysis, and Knowledge Organization, a cura di S.Zani, A.Cerioli, M.Riani e M.Vichi, ed. Springer-Verlag, pp. 271-278.
[7]  Aria, M. (2005). Multi-Class Budget Exploratory Trees. In Studies in Classification, Data Analysis, and Knowledge Organization: New Developments in Classification and Data Analysis, a cura di M. Vichi, P. Monari, S. Mignani, A. Montanari, ed. Springer-Verlag, pp. 3-8.
[8]  Siciliano, R., Aria, M., Conversano, C. (2004). Harvesting trees: methods, software and applications. In Proceedings in Computational Statistics: 16th Symposium of IASC Held in Prague, August 23-27. 2004 (COMPSTAT2004), Eletronical Edition (CD) Physica-Verlag, Heidelberg.
[9]  Aria, M., Mooijaart, A. Siciliano, R., (2003). Neural Budget Networks of Sensorial Data, in M. Schader et al.: Studies in Classification, Data Analysis, and Knowledge Organization, Between Data Science and Applied Data Analysis, Springer-Verlag, XIII, 369-377.
[10]  Aria, M., Mola, F., Siciliano R. (2002). Growing and Visualizing Prediction Paths Trees in Market Basket Analysis, Wolfgang Härdle, Bernd Rönz (Eds.), Proceedings in Computational Statistics: 15th Symposium Held of IASC in Berlin, Germany 2002 (COMPSTAT2002), pp.123-128, Physica-Verlag, Heidelberg.
[11]  Aria, M., D’Ambrosio A, Siciliano R., Tutore V.A. (2011) Indagine statistica sulle aspettative e priorità per soddisfare il turista a Napoli in Rapporto sul Turismo Italiano, 2010-2011 edizione XVII, a cura di Becheri M., ed. Mercury.
[12]  Siciliano, R., Tutore, V.A., Aria, M., D’Ambrosio, A. (2010) Trees with leaves and without leaves in Proceedings of 45th Scientific Meeting of Italian Statistical Society (SIS2010), Padova, ISBN 978 88 6129 566 7.
[13]  Aria, M., Siciliano R. (2009). Il metodo di campionamento, contributo alla ricerca Monitoraggio sul controllo di gestione delle PMI della provincia di Napoli, a cura di Catuogno S., Coppola R., Mauriello P. e Orefice F., Centro Studi dell’Ordine dei Dottori Commercialisti di Napoli, pag. 5-10.
[14]  Aria, M., D’Ambrosio, A., (2008). A non parametric pre-grafting procedure for data fusion, In Proceedings of Metodi, Modelli e Tecnologie dell’Informazione a Supporto delle Decisioni, Università del Salento, Lecce, 18-20 settembre, pag. 333-336, ISBN 978-88-8305-060-2.
[15]  Aria, M., D’Ambrosio, A., Siciliano, R. (2007). Robust Incremental Trees for Missing Data Imputation and Data Fusion, Relazione Invitata in Proceedings of Classification and Data Analysis Group (CLADAG 2007), Edizioni Università di Macerata, 12-14 Settembre, Macerata.
[16]  Siciliano, R., Tutore, V.A., Aria, M. (2007). 3Way Trees, Relazione Invitata in Proceedings of Classification and Data Analysis Group (CLADAG 2007), Edizioni Università di Macerata, 12-14 Settembre, Macerata.