offline web page builder

Antonio D'Ambrosio
Home Page

PhD in Statistics 
Assistant Professor
Department of Economic and Statistics
University of Naples Federico II
Via Cinthia, M.te S. Angelo
80125 Napoli (Italy)
Phone: +39 081 675111
antdambr at unina dot it

                                                                                     Biosketch 


Antonio D'Ambrosio is researcher in Statistics at the Department of Economics and Statistics of the University of Naples Federico II.
He took a degree in Economics at University of Naples Federico II.
From November 2004 to November 2007 he was Ph.D. student at Department of Mathematics and Statistics of the University of Naples Federico II (supervisor prof. dr. Roberta Siciliano). 
In that time he studied at Charles University of Prague (working with prof. dr. Jaromìr Antoch) as well as he studied at Leiden University (working with prof. dr. Willem Heiser and prof. dr. Ab Mooijaart).
He took the Ph.D. in Statistics by defending a Ph.D. thesis named Tree-based methods for Data Editing and Preference Rankings.
He was research assistant at the Department of Mathematics and Statistics of the University of Naples Federico II, working at the European Research Project integrated Web Services Platform for the facilitation of fraud detection in health care e-government service - iWebCare.
He is member of the STAD research group.
He is member of the International Statistical Institute (ISI).
He is member of the International Association for Statistical Computing (IASC).
He is member of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG).
He is member of the Italian Statistical Society (SIS).

Main research interests are classificaton and clustering. Within these frameworks, it's so fascinating dealing with preference rankings. 

Distinguishing marks: Inter supporter!!!

                                                                             Publications

Journal papers


Siciliano, R., D'Ambrosio, A., Aria M., and Amodio, S. Analysis of web visit histories, part II: Predicting navigation by Nested Stump Regression Trees. Journal of Classification. Forthcoming.

D'Ambrosio, A., and Heiser W.J. (2016). A recursive partitioning method for the prediction of preference rankings based upon Kemeny distances. Psychometrika, vol. 81 (3), pp.774-94 DOI: 10.1007/s11336-016-9505-1.

D'Ambrosio, A., Aria, M., Iorio, C and Siciliano, R. (2016). Regression trees for multivalued numerical response variables, Expert systems with applications, DOI: 10.1016/j.eswa.2016.10.021

Iorio, C., Frasso, G., D'Ambrosio, A., and Siciliano R. (2016). Parsimonious Time Series Clustering using P-Splines, Expert Systems with Applications, vol. 52, pp. 26-38. DOI: 10.1016/j.eswa.2016.01.004

Siciliano, R., D'Ambrosio, A., Aria, M. and Amodio, S. (2016) Analysis of web visit histories, part I: Distance-based visualization of sequence rules. Journal of Classification, vol. 33(2), pp. 298-324 DOI: 10.1007/s00357-016-9204-8.

Amodio, S., D'Ambrosio, A. and Siciliano, R. (2016) Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach. European Journal of Operational Research, vol. 249(2), pp. 667-676. DOI: 10.1016/j.ejor.2015.08.048.

D'Ambrosio, A., Amodio, S. and Iorio, C. (2015) Two algorithms for finding optimal solutions of the Kemeny rank aggregation problem for full rankings. Electronic Journal of Applied Statistical Analysis, vol. 8(2), 197-212. DOI: 10.1285/i20705948v8n2p197.

Catuogno, S., Allini, A. and D'Ambrosio, A. (2015). Information Perspective and Determinants of Proportionate Consolidation in Italy. An ante IFRS 11 analysis. Rivista dei Dottori Commercialisti, Fasc. 4, pp. 555-577.

Amodio, S., Aria, M. and D'Ambrosio, A. (2014). On concurvity in nonlinear and nonparametric regression models. Statistica, vol. 24(1), 81-94. DOI: 10.6092/issn.1973-2201/4599

D'Ambrosio A., Aria M. and Siciliano R. (2012). Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm, Journal of Classification, vol. 29(2), pp. 227-258. DOI: 10.1007/s00357-012-9108-1.

Montella A., Aria M., D'Ambrosio A. and Mauriello F. (2012). Data Mining Techniques for Exploratory Analysis of Pedestrian Crashes. Transportation Research Record - Journal of Transportation Research Board. Vol. 2237/2011, pp.107-116. DOI: 10.3141/2237-12. 

Montella A., Aria M., D'Ambrosio A. and Mauriello F. (2011). Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. Accident Analysis & Prevention, vol. 49, pp 58-72, DOI: 10.1016/j.aap.2011.04.025

Montella A., Aria M., D'Ambrosio A., Galante F., Mauriello F. and Pernetti, M. (2011). Simulator evaluation of drivers' speed, deceleration and lateral position at rural intersections in relation to different perceptual cues. Accident Analysis & Prevention, vol. 43(6), pp. 2072-2084, DOI: 10.1016/j.aap.2011.05.030.

Montella A., Aria M., D'Ambrosio A., Galante F., Mauriello F. and 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. DOI: 10.3141/2149-02

Galante F., Mauriello F., Montella A., Pernetti M., Aria M. and D'Ambrosio A. (2010). Traffic Calming Along Rural Highways Crossing Small Urban Communities: a Driving Simulator Experiment, Accident Analysis & Prevention, vol. 42(6), pp. 1585-1594. DOI: 10.1016/j.aap.2010.03.017

D'Ambrosio A. and Tutore V.A. (2009). Kemeny's axiomatic approach to find consensus ranking in tourist satisfaction, Statistica Applicata(Italian Journal of Applied Statistics), vol 20(1), pp. 21-32

                                                                                                                  

Book Chapters

Iorio, C., Aria, M., and D'Ambrosio, A. (2015). A New Proposal for Tree Model Selection and Visualization, in Morlini, I, Minerva, T., Vichi, M. (Eds.) , Advances in Statistical Models for Data Analysis, pp. 149-156. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag, DOI 10.1007/978-3-319-17377-1_16.
 
Heiser W.J. and D'Ambrosio A. (2013). Clustering and Prediction of Rankings within a Kemeny Distance Framework. In Berthold, L., Van den Poel, D, Ultsch, A. (eds). Algorithms from and for Nature and Life.pp-19-31. Springer international. DOI: 10.1007/978-3-319-00035-0_2.

Siciliano R. and D'Ambrosio A. (2012). Statistical monitoring of tourism in the knowledge era. In Morvillo A. (Ed.). Advances in Tourism Studies. McGrow-Hill, pp. 231-258.

Siciliano R., Aria M., D'Ambrosio A. and Tutore V.A. (2011). Indagine statistica sulle aspettative e priorità per soddisfare il turista a Napoli, in Becheri E., Maggiore G. (a cura di), XVII rapporto sul turismo italiano, Franco Angeli, pp. 449-470.

D'Ambrosio A. and Tutore V.A. (2011). Conditional classification trees by weighting the Gini impurity measure, New Perspectives in Statistical Modeling and Analysis, Springer series: Studies in Classification, Data Analysis, and Knowledge Organization, DOI10.1007/978-3-642-11363-5_31, Springer-Verlag Berlin Heidelberg, pp. 273-280

D'Ambrosio A. and Pecoraro M. (2011). Multidimensional Scaling as Visualization tool of Web Sequence Rules, in B. Fichet et al. (eds.), Classification and Multivariate Analysis for Complex Data Structures. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, pp. 307-314. DOI: 10.1007/978-3-642-13312-1_32

Siciliano, R., Aria, M. and D'Ambrosio, A. (2008). Posterior Prediction Modelling of Optimal Trees, in Proceedings in Computational Statistics(COMPSTAT 2008), 18th Symposium Held in Porto, Portugal, Brito, Paula (Ed.), Springer-Verlag, pp. 323-334

D'Ambrosio A., Aria M. and Siciliano R. (2007), Robust Tree-based Incremental Imputation Method for Data Fusion. Lecture notes in computer science 4723(Advances in Intelligent Data Analysis), Springer-Verlag, pp 174-183.

Siciliano R., Aria. and D'Ambrosio A. (2006), Boosted incremental tree-based imputation of missing data, in Data Analysis, Classification and the Forward Search. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag, pp. 271-278.

                                                                                                                

Proceedings

D'Ambrosio, A., Frasso, G., Iorio, C. and Siciliano, R (2015). Probabilistic boosted-oriented clustering of time series. In Mola, Coversano (Eds.), 10th scientific meeting of the Classification and Data Analysis Group, Book of abstracts, p. 61-64, CUEC Editrice.

Iorio, C., D'Ambrosio, A., Frasso, G and Siciliano, R. (2015). Parsimonious clustering of time series. In Mola, Coversano (Eds.), 10th scientific meeting of the Classification and Data Analysis Group, Book of abstracts, p. 226-229, CUEC Editrice.

Mazzeo, G., D'Ambrosio, A. and Siciliano, R. (2015). Accurate algorithms for consensus ranking detection. In Mola, Coversano (Eds.), 10th scientific meeting of the Classification and Data Analysis Group, Book of abstracts, p. 255-258, CUEC Editrice.

Iorio, C., Aria, M. and D'Ambrosio, A. (2013). Visual model representation and selection for classification and regression trees. In Minerva, Morlini, Palumbo (Eds.), 9th meeting of the Classification and Data Analysis Group, Book of short papers, p. 276-279, CLEUP.

D'Ambrosio A. (2012). Missing Data Imputation within the Statistical learning Paradigm. Proceedings of the 46th Scientific Meeting Of The Italian Statistical Society.

Piscitelli A. and D'Ambrosio A. (2012). Assessing assumptions for data fusion procedures. Proceedings of the 46th Scientific Meeting Of The Italian Statistical Society.

Siciliano R., Tutore V.A., Aria M., D'Ambrosio A. (2010). Trees with leaves and without leaves. In 45th scientific meeting of the Italian Statistical Society.

D'Ambrosio A. and Heiser W.J. (2009). Decision Trees for Preference Rankings. Invited talk: Classification and Data Analisys 2009, Book of short papers (Catania, September 9-11, 2009), CLEUP Padova, 133-136.

Tutore V.A. and D'Ambrosio A. (2009).Three-Way Data Analysis by Tree-Based Partitioning. Classification and Data Analisys 2009,Book of short papers (Catania, September 9-11, 2009), CLEUP Padova, 641-644.

D'Ambrosio, A. and Pecoraro M. (2008). Web Structure Mining through implicit behaviors via Multidimensional Scaling, in Proceedings of the First joint meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society(SFC-CLADAG 2008), pp. 261-264.

Aria M. and D'Ambrosio A. (2008). A non parametric pre-grafting procedure for data fusion, Proceedings of the MTISD 2008(Metodi, Modelli e Tecnologie dell’Informzione a Supporto delle Decisioni), Coordinamento SIBA, Università del Salento, pp. 333-336

Giordano G. and D'Ambrosio A. (2008). Multi-Class Budget Tree as weak learner for ensemble procedures, proceedings della XLIV riunione scientifica della Società Italiana di Statistica 

Aria M., D'Ambrosio A. and Siciliano R. (2007), Robust Incremental Trees for Missing Data Imputation and Data Fusion. Classification and Data Analisys 2007, Book of short papers (Macerata, September 12-14, 2007), EUM macerata, 287-290.

Siciliano R., Aria. and D'Ambrosio A. (2005), Boosted stump algorithm for missing data incremental imputation. Invited talk: CLADAG 2005, Book of Short Papers (Parma, June 6-8, 2005), MUP, Parma, 161-164.