Antonio D’Ambrosio
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Publications

Journal Papers

2026: Vannucci, G., D’Ambrosio, A. & Siciliano, R. “Interpretable and robust tree-based methodology for imbalanced classification in driving safety assessment”. Quality and Quantity.  https://doi.org/10.1007/s11135-026-02877-w

2026: D’Ambrosio Alessia, Gismondi, G., Cardillo, M., Pandolfo, G., & D’Ambrosio Antonio “An innovative approach to co-clustering of directional data: a methodological framework with an application on interregional mobility in the Italian national health care system”. Annals of Operations Research, https://doi.org/10.1007/s10479-025-07000-0

2025: Romano, M., Siciliano, C., Conversano, C., & D’Ambrosio, A. “Particle Swarm Optimization for Preference Rankings”. Advances in Data Analysis and Classification, https://doi.org/10.1007/s11634-025-00626-9

2024: Nai Ruscone, M., Fernandez, D., & D’Ambrosio A. “Copula-Based Non-metric Unfolding on Augmented Data Matrix”. Journal of Classification, 41, 678-697.

2024: Baldassarre, A., D’Ambrosio, A., & Conversano C. “Explaining central government’s tax revenue categories through the Bradley-Terry Regression Trunk model”. Statistics and Public Policy, 11(1). 

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

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

2023: Iorio, C., Frasso, G., D’Ambrosio, A., & Siciliano, R. “Boosted-Oriented Probabilistic Smoothing-Spline Clustering of Series”. Statistical Methods and Applications, https://doi.org/10.1007/s10260-022-00665-y 

2021: Cannavacciuolo, L., Postiglione, C., & D’Ambrosio, A. “How to improve the Triage: a dashboard to assess the quality of nurses’ decision-making”. International Journal of Engineering Business Management, DOI: 10.1177/18479790211065558

2021: D’Ambrosio, A., Vera, J. F., & Heiser, W. J. “Avoiding degeneracies in ordinal Unfolding using Kemeny-equivalent dissimilarities for two-way two-mode preference rank data”. Multivariate Behavioral Research, https://doi.org/10.1080/00273171.2021.1899892 

2021: Pandolfo, G., & D’Ambrosio, A. “Depth-based classification of directional data”. Expert Systems with Applications, Vol. 161, 1, 114433, https://doi.org/10.1016/j.eswa.2020.114433

2021: D’Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., & Siciliano, R. “Adjusted concordance index: an extension of the adjusted Rand index to fuzzy partitions”. Journal of Classification, vol. 38(1), pp. 112-128, https://doi.org/10.1007/s00357-020-09367-0

2020: Pandolfo, G., D’Ambrosio, A., Cannavacciuolo, L., & Siciliano, R. “Fuzzy 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

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

2020: Aria, M., D’Ambrosio, A., Iorio, C., Siciliano, R., & Cozza, V. “Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images”. Statistical Papers, vol. 61, 1645–1661, https://doi.org/10.1007/s00362-018-0997-x

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

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

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

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

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

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

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

2018: Pandolfo, G., D’Ambrosio, A., & Porzio, G. “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

2018: Iorio, C., Frasso, G., D’Ambrosio, A., & Siciliano, R. “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

2017: D’Ambrosio, A., Mazzeo, G., Iorio, C., & Siciliano, R. “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

2017: D’Ambrosio, A., Aria, M., Iorio, C., & Siciliano, R. “Regression trees for multivalued numerical response variables”. Expert Systems with Applications, vol. 62, pp. 21-28, DOI: 10.1016/j.eswa.2016.10.021

2017: Siciliano, R., D’Ambrosio, A., Aria M., & Amodio, S. “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

2016: D’Ambrosio, A., & Heiser W. J. “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

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

2016: Siciliano, R., D’Ambrosio, A., Aria, M., & Amodio, S. “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

2016: Amodio, S., D’Ambrosio, A., & Siciliano, R. “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

2015: D’Ambrosio, A., Amodio, S., & Iorio, C. “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

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

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

2012: D’Ambrosio A., Aria M., & Siciliano R. “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

2012: Montella A., Aria M., D’Ambrosio A., & Mauriello F. “Data Mining Techniques for Exploratory Analysis of Pedestrian Crashes”. Transportation Research Record, Vol. 2237/2011, pp. 107-116, DOI: 10.3141/2237-12

2011: Montella A., Aria M., D’Ambrosio A., & Mauriello F. “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

2011: Montella A., Aria M., D’Ambrosio A., Galante F., Mauriello F., & Pernetti, M. “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

2010: Montella A., Aria M., D’Ambrosio A., Galante F., Mauriello F., & Pernetti, M. “Perceptual Measures to Influence Operating Speeds and Reduce Crashes at Rural Intersections”. Transportation Research Record, vol. 2149, pp. 11-20, DOI: 10.3141/2149-02

2010: Galante F., Mauriello F., Montella A., Pernetti M., Aria M., & D’Ambrosio A. “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

2009: D’Ambrosio A., & Tutore V.A. “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

2020: Sciandra, M., D’Ambrosio, A., & Plaia, A. “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), https://doi.org/10.1002/9781119779841.ch11

2018: Iorio C., Frasso G., D’Ambrosio A., & Siciliano R. “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, Cham, https://doi.org/10.1007/978-3-319-78744-2_20

2015: Iorio, C., Aria, M., & D’Ambrosio, A. “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-Verlag, https://doi.org/10.1007/978-3-319-17377-1_18

2013: Heiser W. J., & D’Ambrosio A. “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, https://doi.org/10.1007/978-3-319-00035-0_2

2011: D’Ambrosio A., & Tutore V. A. “Conditional classification trees by weighting the Gini impurity measure”. New Perspectives in Statistical Modeling and Analysis, Springer-Verlag, pp. 273-280, https://doi.org/10.1007/978-3-642-11363-5_31

2011: D’Ambrosio A., & Pecoraro M. “Multidimensional Scaling as Visualization tool of Web Sequence Rules”. In: B. Fichet et al. (eds.), Classification and Multivariate Analysis for Complex Data Structures. Springer-Verlag, pp. 307-314, https://doi.org/10.1007/978-3-642-13366-4_33

2008: Siciliano, R., Aria, M., & D’Ambrosio, A. “Posterior Prediction Modelling of Optimal Trees”. In: Proceedings in Computational Statistics (COMPSTAT 2008), Porto, Portugal. Springer-Verlag, pp. 323-334, https://doi.org/10.1007/978-3-7908-2084-3_27

2007: D’Ambrosio A., Aria M., & Siciliano R. “Robust Tree-based Incremental Imputation Method for Data Fusion”. Lecture notes in computer science 4723, Springer-Verlag, pp. 174-183, https://doi.org/10.1007/978-3-540-74825-0_16

2006: Siciliano R., Aria M., & D’Ambrosio A. “Boosted incremental tree-based imputation of missing data”. In: Data Analysis, Classification and the Forward Search. Springer-Verlag, pp. 271-278, https://doi.org/10.1007/3-540-35980-8_31

Refereed Proceedings

2025: Cardillo, M., Gismondi, G., D’Ambrosio, A., D’Ambrosio, A., & Pandolfo, G. “A Novel Framework for Co-clustering on Directional Data: Adapting the Depth-Based Medoids Clustering Algorithm (DBMCA) with
an Application”. In: Statistics for Innovation II, Springer Cham, pp. 343-348.

2025: Leone, F., Meccariello, A., Rossi, D., Ruocco, L., Santone, C., Beraldo, S., D’Ambrosio, A., Iorio, C., & Puopolo, G. W. “From Data To Decision: A Scalable AI Approach To Public And Private Funding Discovery”. In: 15th Scientific Meeting of the Classification and Data Analysis Group, Book of Abstracts, Zaccaria Editore, Naples, p. 288.

2025: Leone, F., Meccariello, A., Rossi, D., Ruocco, L., Santone, C., Beraldo, S., D’Ambrosio, A., Iorio, C., & Puopolo, G. W. “Enhancing Access To European Funding Through AI-Powered Tool”. In: 15th Scientific Meeting of the Classification and Data Analysis Group, Book of Abstracts, Zaccaria Editore, Naples, pp. 286-287.

2025: Leone, F., Meccariello, A., Rossi, D., Ruocco, L., Santone, C., Beraldo, S., D’Ambrosio, A., Iorio, C., & Puopolo, G. W. “Can GenAI Match Human Standards In Financial Reports?” In: 15th Scientific Meeting of the Classification and Data Analysis Group, Book of Abstracts, Zaccaria Editore, Naples, p. 285.

2025: Ruscone, M. N., & D’Ambrosio, A. “Non-metric Unfolding via Copula”. In: Methodological and Applied Statistics and Demography I, Springer Cham, pp. 46-49. 2025: Gismondi, G., Coraggio, L., & D’Ambrosio, A. “Tackling Noise in Ranking Models with the Boosting Paradigm”. In: Statistics for Innovation III, Springer Nature Switzerland, pp. 345-349.

2025: Coraggio, L., D’Ambrosio, A., & Mirkin, B. “Analysis of the Mirkin’s Distance on Binary Relations for Clustering Stability”. In: Statistics for Innovation I, Springer Nature Switzerland, pp. 496-502. 2023: Iorio, C., Pandolfo, G., & D’Ambrosio, A. “A proposal to evaluate the solution of fuzzy clustering algorithms”. In: Coretto, P., Giordano, G., La Rocca, M., Parrella, M. L., & Rampichini, C. (Eds). CLADAG 2023. Book of abstracts and short papers, p. 520-523, Pearson.

2022: Nai Ruscone, M., D’Ambrosio, A., & Fernandez, D. “Copula-based non-metric unfolding”. In: Luati, A. & Ferraro, M. B. (Eds.). Proceedings of COMPSTAT and SDS 2022, p. 68. 2022: Nai Ruscone, M., & D’Ambrosio, A. “Copula-based Non-Metric Unfolding on Augmented Data Matrix”. In: Classification and Data Science in the Digital Age- Book of Abstracts IFCS 2022, p. 113, Instituto Nacional de Estatística, Lisbon (Portugal).

2020: Baldassarre, A., Conversano, C., D’Ambrosio, A., De Rooij, M., & Dusseldorp, E. “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, Milan.

2020: Nai Ruscone, M., & D’Ambrosio, A. “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, Milan.

2019: Feijt, A. A., Mol, S. E., Espin, C. A., D’Ambrosio, A., & Heiser, W. J. “Instructional factors that influence learning from university lectures: Opinions of students with and without disabilities”. 1st SRLD Conference, Padua.

2019: Nai Ruscone, M., & D’Ambrosio, A. “Copula-Based Non-Metric Unfolding on Augmented Data Matrix”. In: G. C. Porzio, F. Greselin, S. Balzano (Eds.), 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG 2019), pp. 357–360, University of Cassino and Southern Lazio, Cassino (Italy).

2019: D’Ambrosio, A., Conversano, C., & Ingrassia, S. “The ANVUR’s system assessing the perceived quality of professors’ teaching effectiveness: defining a suitable performance indicator”. In: M. Bini, P. Amenta, A. D’Ambra, I. Camminatiello (Eds.), Statistical Methods for Service quality evaluation, proceedings of the 9th International Conference IES 2019, pp. 43-47, Cuzzolino, Naples (Italy).

2019: Baldassarre, A., Conversano, C., & D’Ambrosio, A. “Detecting and interpreting the consensus ranking based on the weighted Kemeny distance”. In: G. C. Porzio, F. Greselin, S. Balzano (Eds.), 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG 2019), pp. 57–60, University of Cassino and Southern Lazio, Cassino (Italy).

2019: D’Ambrosio, A., Baldassarre, A., & Conversano, C. “Simultaneous Threshold Interaction Modeling Approach for Paired Comparisons Rankings”. In: Skiadas C. H. (Ed.), proceedings of the 18th Applied Stochastic Models and Data Analysis International Conference, p. 59.

2019: Scaglione, M., Iorio, C., & D’Ambrosio, A. “Silhouette-based method for portfolio selection”. In: G. C. Porzio, F. Greselin, S. Balzano (Eds.), 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG 2019), pp. 424–427, University of Cassino and Southern Lazio, Cassino (Italy).

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

2018: Pandolfo, G., Iorio, C., & D’Ambrosio, A. “Depth-based portfolio selection”. In: A. Abruzzo, E. Brentari, M. Chiodi, D. Piacentino (Eds.), proceedings of the 49th Scientific Meeting of the Italian Statistical Society, pp. 1-6, Pearson.

2017: Iorio, C., & D’Ambrosio, A. “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, p. 11-16, Universitas Studiorum, Mantua.

2017: D’Ambrosio, A., Iorio, C., & Siciliano, R. “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, p. 1-6, Universitas Studiorum, Mantua.

2015: D’Ambrosio, A., Frasso, G., Iorio, C., & Siciliano, R. “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.

2015: Iorio, C., D’Ambrosio, A., Frasso, G., & Siciliano, R. “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.

2015: Mazzeo, G., D’Ambrosio, A., & Siciliano, R. “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.

2013: Iorio, C., Aria, M., & D’Ambrosio, A. “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.

2012: D’Ambrosio A. “Missing Data Imputation within the Statistical learning Paradigm”. Proceedings of the 46th Scientific Meeting Of The Italian Statistical Society.

2012: Piscitelli A., & D’Ambrosio A. “Assessing assumptions for data fusion procedures”. Proceedings of the 46th Scientific Meeting Of The Italian Statistical Society.

2010: Siciliano R., Tutore V. A., Aria M., & D’Ambrosio A. “Trees with leaves and without leaves”. In: 45th scientific meeting of the Italian Statistical Society.

2009: D’Ambrosio A., & Heiser W. J. “Decision Trees for Preference Rankings”. Invited talk: Classification and Data Analisys 2009, Book of short papers, CLEUP Padua, 133-136.

2009: Tutore V. A., & D’Ambrosio A. “Three-Way Data Analysis by Tree-Based Partitioning”. Classification and Data Analisys 2009, Book of short papers, CLEUP Padua, 641-644.

2008: D’Ambrosio, A., & Pecoraro M. “Web Structure Mining through implicit behaviors via Multidimensional Scaling”. In: Proceedings of the First joint meeting of the SFC-CLADAG 2008, pp. 261-264.

2008: Aria M., & D’Ambrosio A. “A non parametric pre-grafting procedure for data fusion”. Proceedings of the MTISD 2008, Coordinamento SIBA, University of Salento, pp. 333-336.

2008: Giordano G., & D’Ambrosio A. “Multi-Class Budget Tree as weak learner for ensemble procedures”. In: Proceedings della XLIV riunione scientifica della Società Italiana di Statistica.

2007: Aria M., D’Ambrosio A., & Siciliano R. “Robust Incremental Trees for Missing Data Imputation and Data Fusion”. Classification and Data Analisys 2007, Book of short papers, EUM Macerata, 287-290.

2005: Siciliano R., Aria M., & D’Ambrosio A. “Boosted stump algorithm for missing data incremental imputation”. Invited talk: CLADAG 2005, Book of Short Papers, MUP, Parma, 161-164.