Peer-reviewed journal articles

  1. Greselin, F., Zaccaria, G. (2024)
    Studying hierarchical latent structures in heterogeneous populations with missing information
    Journal of Classification, doi: 10.1007/s00357-024-09492-0.
    Link
  2. Cavicchia, C., Vichi, M., Zaccaria, G. (2024)
    Parsimonious Ultrametric Gaussian Mixture Models
    Statistics and Computing, 34, 108.
    Link
  3. Cavicchia, C., Sarnacchiaro, P., Vichi, M., Zaccaria, G. (2024)
    A model-based ultrametric composite indicator for studying waste management in Italian municipalities
    Computational Statistics, 39, 21-50.
    Link
  4. Cavicchia, C., Vichi, M., Zaccaria, G. (2023)
    Hierarchical Disjoint Principal Component Analysis
    AStA Advances in Statistical Analysis, 107(3), 537–574.
    Link
  5. Cavicchia, C., Vichi, M., Zaccaria, G. (2022)
    Gaussian mixture model with an extended ultrametric covariance structure
    Advances in Data Analysis and Classification, 16(2), 399-427.
    Link
  6. Cavicchia, C., Vichi, M., Zaccaria, G. (2020)
    The ultrametric correlation matrix for modelling hierarchical latent concepts
    Advances in Data Analysis and Classification, 14(4), 837-853.
    Link

Peer-reviewed articles in edited volumes and Ph.D. thesis

  1. Mingione, M., Vichi, M., Zaccaria, G. (2022)
    Complex Dimensionality Reduction: Ultrametric Models for Mixed-Type Data.
    In: L.A. Garcia-Escudero, A. Gordaliza, A. Mayo, M. Asunción Lubiano Gomez, M. Angeles Gil, P. Grzegorzewski, O. Hryniewicz (Eds.) Building Bridges between Soft and Statistical Methodologies for Data Science. SMPS 2022. Advances in Intelligent Systems and Computing, vol 1433. Springer, Cham. doi: 10.1007/978-3-031-15509-3_37
    Link
  2. Zaccaria, G. (2022)
    Ultrametric models for hierarchical dimensionality reduction.
    Ph.D. Thesis.
    Link
  3. Cavicchia, C., Vichi, M., Zaccaria, G. (2020)
    Exploring hierarchical concepts: theoretical and application comparisons.
    T. Imaizumi, A. Nakayama, S. Yokoyama, S. (Eds.), Advanced Studies in Behaviormetrics and Data Science. Behaviormetrics: Quantitative Approaches to Human Behavior, vol. 5 (pp. 315-328). Springer, Singapore.
    Link

Peer-reviewed conference articles

  1. Zaccaria, G. (2023)
    Ultrametric Gaussian Mixture Models With Parsimonious Structures
    P. Coretto, G. Giordano, M. La Rocca, M.L. Parrella, C. Rampichini (Eds.), Book of Abstract and Short Papers CLADAG 2023 (pp. 314-317). Pearson.
    ISBN: 978-88-9193-563-2.
  2. Greselin, F., Zaccaria, G. (2023)
    Handling missing data in complex phenomena: an ultrametric model-based approach for clustering
    F. M. Chelli, M. Ciommi, S. Ingrassia, F. Mariani, M. C. Recchioni (Eds.). Book of Short Papers SIS 2023 (pp. 961-996). Pearson.
    ISBN: 978-88-9193-561-8AAVV.
  3. Zaccaria, G., Sarnacchiaro P. (2022)
    An ultrametric model for building a composite indicator system to study climate change in European countries
    A. Balzanella, M. Bini, C. Cavicchia, R. Verde. (Eds.), Book of Short Papers SIS 2022 (pp. 970-974). Pearson.
    ISBN: 978-88-9193-231-0.
  4. Cavicchia, C., Sarnacchiaro, P., Vichi, M., Zaccaria, G. (2022)
    An ultrametric model to build a Composite Indicators system
    R. Lombardo, I. Camminatiello, V. Simonacci (Eds.), Book of Short papers, 10th International Conference IES 2022 Innovation & Society 5.0: Statistical and Economic Methodologies for Quality Assessment, Department of Economics, University of Campania “L. Vanvitelli”, January 27th - 28th 2022 (pp. 208-211). Sesto San Giovanni: PKE - Professional Knowledge Empowerment s.r.l.
    ISBN: 978-88-94593-35-8.
  5. Cavicchia, C., Vichi, M., Zaccaria, G. (2021)
    A parsimonious parameterization of a nonnegative correlation matrix
    S. Ingrassia, A. Punzo, R. Rocci (Eds.), MBC2 2020 Book of Short Papers of the 5th International Workshop on Models and Learning for Clustering and Classification, Catania (pp. 21-26). Milano: Le Edizioni.
    ISBN: 978-88-5526-539-3.
  6. Cavicchia, C., Vichi, M., Zaccaria, G. (2021)
    Model-based clustering with parsimonious covariance structure
    G. C. Porzio, C. Rampichini, C. Bocci (Eds.), CLADAG 2021 Book of Abstracts and Short Papers. 13th Scientific Meeting of the Classification and Data Analysis Group, Firenze, September 9-11, 2021 (pp. 296-299). Firenze: Firenze University Press.
    ISBN: 978-88-5518-340-6.
  7. Cavicchia, C., Vichi, M., Zaccaria, G. (2021)
    The ultrametric covariance model for modelling teachers’ job satisfaction
    C. Perna, N. Salvati, F. Schirripa Spagnolo (Eds.), Book of Short Papers SIS 2021 (pp. 1319-1324). Pearson.
    ISBN: 978-88-9192-736-1.
  8. Zaccaria, G., Vichi, M. (2020)
    Exploring drug consumption via an ultrametric correlation matrix
    A. Pollice, N. Salvati, F. Schirripa Spagnolo (Eds.), Book of Short Papers SIS 2020 (pp. 372-377). Pearson.
    ISBN: 978-88-9191-077-6.
  9. Cavicchia, C., Vichi, M., Zaccaria, G. (2019)
    Dimensionality reduction via hierarchical factorial structure
    G. C. Porzio, F. Greselin, S. Balzano (Eds.), Book of Short Papers, CLADAG 2019, 11-13 September 2019, Cassino (pp. 116-119). Cassino: Centro Editoriale di Ateneo Università di Cassino e del Lazio Meridionale.
    ISBN: 978-88-8317-108-6.
  10. Cavicchia, C., Vichi, M., Zaccaria, G. (2019)
    A new hierarchical model-based composite indicator on climate change
    M. Bini, P. Amenta, A. D’Ambra, I. Camminatiello (Eds.), Statistical Methods for Service Quality Evaluation, Book of Short Papers of IES 2019 in Rome, Italy, 4-5 July (pp. 346-349). Cuzzolin.
    ISBN: 978-88-86638-65-4.
  11. Cavicchia, C., Vichi, M., Zaccaria, G. (2019)
    Hierarchical Clustering and Dimensionality Reduction for Big Data
    G. Arbia, S. Peluso, A. Pini, G. Rivellini (Eds.), Smart Statistics for Smart Applications, Book of Short Papers SIS 2019 (pp. 173-180). Pearson.
    ISBN: 978-88-9191-510-8.