Research

In my (short, I have to say) research activity I’ve mainly focused in developing tools and methodologies pertaining, broadly speaking, to the cluster analysis realm.

More specifically I am particularly interested in the density-based formulation of the clustering problem both from a model-based (parametric) and from a modal (nonparametric) standpoint.

During my PhD I’ve been lucky enough to work on theoretical as well as application-motivated issues. My recent works thus include nonparametric density estimation when considered as a tool for the final purpose of grouping the data at hand, ensamble clustering approaches and simultaneous clustering of subjects and variables when dealing with time-dependent observations.

Currently I’m working with Prof. Brendan Murphy on some high-dimensional problems arising in the framework of the VistaMilk project.

Interests

  • Clustering
  • Density estimation
  • Nonparametric tools
  • Latent variable models
  • Time-dependent data
  • Dimensionality reduction

Publications

Working Papers

  • Casa, A., Bouveyron, C., Erosheva, E. & Menardi, G. (2019+).
    Co-clustering of time-dependent data via Shape Invariant Model.

Under Review

  • Casa, A., Scrucca, L. & Menardi, G. (2019).
    How bettering the best? Answers via model ensemble in density-based clustering.
    Pre-print available at https://arxiv.org/pdf/1911.06726.pdf
  • Casa, A. & Menardi, G. (2019).
    Nonparametric semi-supervised classification with application to signal detection in High Energy Physics.
    Pre-print available at https://arxiv.org/pdf/1809.02977.pdf

Journal Articles

  • Casa, A., Chacón, J.E. & Menardi, G. (2020).
    Modal clustering asymptotics with applications to bandwidth selection.
    Electronic Journal of Statistics (in press). Pre-print available at https://arxiv.org/pdf/1901.07300.pdf
  • Casa, A. (2018).
    On the choice of the weight function for the integrated likelihood.
    SM Journal of Biometrics & Biostatistics, 3(3), 1033.

Book Chapters

  • Cabassi A., Casa, A., Farcomeni, A., Fontana, M., Russo, M. (2018).
    Three testing perspectives on connectome data.
    Studies in Neural Data Science, Springer Volume ‘Proceedings in Mathematics & Statistics’, ISBN-9783030000394.

Conferences and Workshops

  • Casa, A., Chacon, J.E. & Menardi, G. (2019).
    Asymptotics for bandwidth selection in nonparametric clustering.
    CLADAG 2019 - Book of Short Papers.
  • Casa, A. & Menardi, G. (2019).
    Nonparametric Semisupervised Classification and Variable Selection for New Physics Searches.
    EMS 2019 - Program and Book of Abstracts.
  • Pascali, G., Casa, A. & Menardi, G. (2019).
    Co-clustering TripAdvisor data for personalized recommendations.
    Book of short papers SIS 2019. ISBN: 978889191510.
  • Casa, A., Scrucca, L. & Menardi, G. (2018).
    Averaging via stacking in model-based clustering.
    Book of Abstracts of the 4th International Workshop on Model-Based Clustering and Classification (MBC2).
  • Casa, A., Scrucca, L. & Menardi, G. (2018).
    On the selection uncertainty in parametric clustering.
    Book of Abstracts of the European Conference on Data Analysis (ECDA).
  • Casa, A., Chacon, J.E. & Menardi, G. (2018).
    On the choice of an appropriate bandwidth for modal clustering.
    Book of short papers SIS 2018. ISBN: 9788891910233.
  • Casa, A., Chacon, J.E. & Menardi, G. (2018).
    Clustering-oriented selection of the amount of smoothing in kernel density estimation.
    Book of Abstracts ISNPS 2018. ISBN:978-88-61970-00-7.
  • Casa, A. & Menardi, G. (2017).
    Signal detection in high energy physics via a semisupervised nonparametric approach.
    Proceedings of the Conference of the Italian Statistical Society “Statistics and Data Sciences: new challenges, new generations”. ISBN: 978-88-6453-521-0.
  • Casa, A. & Menardi, G. (2017).
    Nonparametric semi-supervised classification with application to signal detection in high energy physics.
    Book of Abstracts of the International Federation of Classification Societies (IFCS).