**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 was visiting researcher at the Department of Psychology - Section methods and statistics - of the
Leiden University (The Netherands).

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**

D'Ambrosio, A., Mazzeo, G., Iorio, C., and 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 and 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., and Amodio, S. (2017) Analysis of web visit histories, part II:
Predicting navigation by Nested Stump Regression Trees. * Journal of Classification*. DOI: 10.1007/s00357-017-9239-5.

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.

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

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.

Iorio, C., and 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*,
p. 11-16, Universitas Studiorum, Mantova

D'Ambrosio, A., Iorio, C., and 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*,
p. 1-6, Universitas Studiorum, Mantova

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.