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Antonio D'Ambrosio
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PhD in Statistics 
Full 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 Full Professor 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 was visiting researcher at the Department of Psychology - Section methods and statistics - of the Leiden University (The Netherands).
He was visiting researcher at the Department of Statistics and Operations Research of the University of Granada (Spain).
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).
He is member of the American Statistical Society (ASA).

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


Baldassarre, A., D'Ambrosio, A., & Conversano C. (2024). Explaining central government’s tax revenue categories through the Bradley-Terry Regression Trunk model. Statistics and Public Policy, Accepted manuscript.

Pandolfo, G., & D'Ambrosio, A. (2023). Clustering directional data through depth functions. Computational Statistics, 38, pp. 1487-1506, https://doi.org/10.1007/s00180-022-01281-w .

Baldassarre, A., Dusseldorp, E., D'Ambrosio, A., de Rooij, M., & Conversano, C. (2023). The Bradley-Terry Regression Trunk approach for modelling preference data with small trees. Psychometrika, 88, pp. 1443-1465, https://doi.org/10.1007/s11336-022-09882-6 .

Iorio, C., Frasso, G., D'Ambrosio, A., & Siciliano, R. (2023). Boosted-oriented probabilistic smoothing-spline clustering of series. Statistical Methods and Applications, 32, pp. 1123–1140, https://doi.org/10.1007/s10260-022-00665-y .

D'Ambrosio, A., Vera J.F. & Heiser, W.J. (2022). Avoiding degeneracies in ordinal Unfolding using Kemeny-equivalent dissimilarities for two-way two-mode preference rank data. Multivariate Behavioral Research, vol. 57(4), p. 679-699, https://doi.org/10.1080/00273171.2021.1899892.

Pandolfo, G. & D'Ambrosio, A. (2021). Depth-based classification of directional data. Expert Systems with Applications, vol. 169, 114433, https://doi.org/10.1016/j.eswa.2020.114433 .

Cannavacciuolo, L., Ponsiglione, C. & D'Ambrosio, A. (2021). How to improve the Triage: A dashboard to assess the quality of nurses' decision-making. International Journal of Engineering Business Management, https://doi.org/10.1177/18479790211065558.

D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G. & Siciliano, R. (2021). Adjusted concordance index: an extension of the adjusted Rand index to fuzzy partitions. Journal of Classification, vol. 38, p. 112-128, https://doi.org/10.1007/s00357-020-09367-0.

Pandolfo, G., D'Ambrosio, A., Cannavacciuolo, L. & Siciliano, R. (2020). Logic AGgregation of Crisp Data Partitions as Learning Analytics in Triage Decisions. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.113512.

Iorio, C., Pandolfo, G., Frasso, G. & D'Ambrosio, A. (2020). A combined clustering and multi-criteria approach for portfolio selection. Statistica & Applicazioni. DOI: 10.26350/999999_000018.

Pandolfo, G., Iorio, C., Siciliano, R. & D'Ambrosio, A. (2019). Robust mean-variance portfolio through the weighted Lp depth function. Annals of Operations Research, https://doi.org/10.1007/s10479-019-03474-x

Iorio, C., Pandolfo, G., D'Ambrosio, A. & Siciliano, R. (2019). Mining big data in tourism. Quality & Quantity, https://doi.org/10.1007/s11135-019-00927-0

Scandurra, A., Alterisio, A., Di Cosmo, A., D’Ambrosio, A. & D’Aniello, B. (2019). Ovariectomy impairs socio-cognitive functions in dogs. Animals, 9(2), 58, pp. 1-7.

Iorio, C., Aria, M., D'Ambrosio, A. & Siciliano, R. (2019). Informative Trees by Visual Pruning. Expert Systems with Applications, vol. 127, pp. 228-240, https://doi.org/10.1016/j.eswa.2019.03.018

D'Ambrosio, A., Iorio, C., Staiano, M. & Siciliano, R. (2019). Median constrained bucket order rank aggregation. Computational Statitstics, vol. 34(2), pp. 787–802, https://doi.org/10.1007/s00180-018-0858-z

D'Ambrosio, A. & Heiser, W.J. (2019). A Distribution-free Soft Clustering Method for Preference Rankings. Behaviormetrika , vol. 46(2), pp. 333–351, DOI: 10.1007/s41237-018-0069-5

Morrone, A., Piscitelli, A. & D'Ambrosio, A. (2019). How Disadvantages Shape Life Satisfaction: an Alternative Methodological Approach. Social Indicator Research, vol. 141(1), pp. 477-502, https://doi.org/10.1007/s11205-017-1825-8

Pandolfo, G., D'Ambrosio, A. & Porzio, G. (2018). A note on depth-based classification of circular data. Electronic Journal of Applied Statistical Analysis, vol. 11(2), pp. 447-462, DOI: 10.1285/i20705948v11n2p447

Aria, M., D'Ambrosio, A., Iorio, C., Siciliano, R. & Cozza, V. (2018). Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images. Statistical papers , DOI: 10.1007/s00362-018-0997-x

Iorio, C., Frasso, G., D'Ambrosio, A. & Siciliano, R. (2018). A P-spline based clustering approach for portfolio selection. Expert systems with applications , vol. 95, pp. 88-103. DOI: 10.1016/j.eswa.2017.11.031. Online first: November 14, 2017.

D'Ambrosio, A., Mazzeo, G., Iorio, C. & Siciliano, R. (2017). A differential evolution algorithm for finding the median ranking under the Kemeny axiomatic approach. Computers and Operations Research, vol. 82, pp. 126-138. DOI: 10.1016/j.cor.2017.01.017.

D'Ambrosio, A., Aria, M., Iorio, C. & Siciliano, R. (2017). Regression trees for multivalued numerical response variables, Expert systems with applications, vol. 62, pp. 21-28, DOI: 10.1016/j.eswa.2016.10.021

Siciliano, R., D'Ambrosio, A., Aria M. & Amodio, S. (2017) Analysis of web visit histories, part II: Predicting navigation by Nested Stump Regression Trees. Journal of Classification, vol. 34(3), pp. 473-493. DOI: 10.1007/s00357-017-9239-5.

D'Ambrosio, A. & 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.

Iorio, C., Frasso, G., D'Ambrosio, A. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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

Sciandra, M., D'Ambrosio, A. & Plaia, A. (2020). Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix. In: Makrides A., Karagrigoriou A., Skiadas C.H. (eds). Data Analysis and Applications 3 , Chapter 11, pp. 215-229. Iste-Wiley, London (UK).

Iorio C., Frasso G., D'Ambrosio A. & Siciliano R. (2018). P-Splines Based Clustering as a General Framework: Some Applications Using Different Clustering Algorithms. In: Mola F., Conversano C., Vichi M. (eds). Classification, (Big) Data Analysis and Statistical Learning, pp 183-190. Springer series: Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. DOI: 10.1007/978-3-319-55708-3_20.

Iorio, C., Aria, M. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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

Iorio, C., Pandolfo, G. and D'Ambrosio, A. (2023). A proposal to evaluate the solution of fuzzy clustering algorithms. In Coretto, P., Giordano, G., La Rocca, M., Parrella, ML.& Rampichini, C. (Eds.), CLADSAG 2023. Book of Short Papers, pp. 520-523, Pearson Italy

Baldassarre, A., Concersano, C., D'Ambrosio, A., De Rooij, M & Dusseldorp, E. (2020). Discovering Interaction Effects Between Subject-Specific Covariates: A New Probabilistic Approach For Preference Data. In Pollice, A., Salvati, N & Schirripa Spagnolo, F. (Eds.), Proceedings of the 50th Scientific Meeting Of The Italian Statistical Society, pp. 1166-1170, Pearson Italia, Milano.

Nai Ruscone, M. & D'Ambrosio, A. (2020). Non-metric unfolding on augmented data matrix: a copula-based approach. In Pollice, A., Salvati, N & Schirripa Spagnolo, F. (Eds.), Proceedings of the 50th Scientific Meeting Of The Italian Statistical Society, pp. 1189-1193, Pearson Italia, Milano.

Feijt A.A.., Mol S.E., Espin C.A., D'Ambrosio A. & Heiser W.J. (2019). Instructional factors that influence learning from university lectures: Opinions of students with and without disabilities. Proceedings of the 1st Society for Research on Learning Disorders (SRLD) Conference, Padua.

D’Ambrosio A., Conversano C. & Ingrassia S. (2019). The ANVUR’s system assessing the perceived quality of professors’ teaching effectiveness: defining a suitable performance indicator. In "Proceedings of the Conference Innovation and Society 2029 - Statistical Methods for Evaluation", Roma (Italy), July 4-5, 2019, Cuzzolin Editore, Roma, 43-47

Pandolfo, G., Iorio, C. & D'Ambrosio, A. (2018). Depth-based portfolio selection. In Abbruzzo, A., Brentari, E., Chiodi, M. & Piacentino, D. (Eds.), Proceedings of the 49th Scientific Meeting Of The Italian Statistical Society, pp. 1061-1066, Pearson Italia, Milano.

Sciandra, M., D'Ambrosio, A. & Plaia, A. (2018). A Projection Pursuit Algorithm for Preference Data. In Christos H. Skiadas (Ed.), Proceedings of the 5th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop, p. 101, ISAST, Athens.

Iorio, C. & D'Ambrosio, A. (2017). Time Series Clustering for Portfolio Selection. In F. Greselin, F. Mola, Ma. Zenga (Eds.), 11th Scientific Meeting of the CLAssification and Data Analysis Group of the Italian Statistical Society, pp. 11-16, Universitas Studiorum, Mantova

D'Ambrosio, A., Iorio, C. & Siciliano, R. (2017). Constrained consensus bucket order. In F. Greselin, F. Mola, Ma. Zenga (Eds.), 11th Scientific Meeting of the CLAssification and Data Analysis Group of the Italian Statistical Society, pp. 1-6, Universitas Studiorum, Mantova

D'Ambrosio, A., Frasso, G., Iorio, C. & 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, pp. 61-64, CUEC Editrice.

Iorio, C., D'Ambrosio, A., Frasso, G & 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, pp. 226-229, CUEC Editrice.

Mazzeo, G., D'Ambrosio, A. & 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, pp. 255-258, CUEC Editrice.

Iorio, C., Aria, M. & 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, pp. 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. & 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. & 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, pp. 133-136.

Tutore V.A. & 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, pp. 641-644.

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

Aria M. & 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. & 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. & 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, pp. 287-290.

Siciliano R., Aria. & 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, pp. 161-164.