Curriculum Vitae

Assegnista di Ricerca in Statistica, presso il Dipartimento di Istituzioni Pubbliche, Economia e Società, Facoltà di Scienze Politiche, Università di Roma Tre. (Supervispor: Prof. Francesco Lagona)

Assegnista di Ricerca in Statistics presso il Dipartimento di Statistica, Penn State University (USA). (Supervisors: Prof. Bruce Lindsay & Dr. Qunhua Li), Gennaio 2015-Gennaio 2016

Dottorato di Ricerca in Statistica Metodologica, Università degli studi di Roma “Sapienza”. (Supervisor: Prof. Roberto Rocci) Dicembre 2014.

Visiting PhD student presso il Dipartimento di Statistica. (Supervisor: Prof. Bruce Lindsay), Penn State University (USA). Gennaio-Dicembre 2014.

MSc in Statistics (with distinction), University of Warwick (UK). Dicembre 2012.

MSc in Economics (cum summa laude with honors), Università di Tor Vergata, Roma. Luglio 2011,

BSc in Economics (cum summa laude),  Università di Tor Vergata, Roma. Ottobre 2009.

Research interests

Finite Mixture Models

Composite Likelihood Methods

Classification and Dimensionality Reduction Techniques

Spatial Statistics

Pubblicazioni recenti

Ranalli, M., Rocci, R. (2017). A Model-Based Approach to Simultaneous Clustering and Dimensional Reduction of Ordinal Data. Psychometrika. https://doi.org/10.1007/s11336-017-9578-5

Ranalli, M., Lagona, F., Picone, M. and Zambianchi, E. (2017), Segmentation of sea current fields by cylindrical hidden Markov models: a composite likelihood approach. J. R. Stat. Soc. C. doi:10.1111/rssc.12240

Ranalli, M., Rocci, R. (2017). Mixture models for mixed-type data through a composite likelihood approach. Computational Statistics & Data Analysis, 110, 87–102.

Ranalli, M. (2016). New perspective on likelihood-based inference for latent and observed Gaussian mixture models.  Best Ph.D. Theses in Statistics and Applications. SIS-CLEUP (in printing).

Ranalli, M., Rocci, R. (2016). Standard and novel model selection criteria in the pairwise likelihood estimation of a mixture model for ordinal data p. 5368 in Studies in Classification, Data Analysis, and Knowledge Organization. Analysis of Large and Complex Data. Editors: Wilhelm, A.F.X. and Kestler, H.A. ISBN978-3-319-25224-7.

Ranalli, M., Rocci, R. (2016). Mixture Models for Ordinal Data: A Pairwise Likelihood Approach, Statistics and Computing, 26(1), 529–547.

Ranalli, M., Rocci, R. (2015). A pairwise likelihood approach to simultaneous clustering and dimensional reduction of ordinal data, arXiv preprint: 1504.02913 (Revised version submitted). (Ranked third in the paper competition, held during the IFCS 2015 Conference).

Ranalli, M., Rocci, R. (2015). Clustering methods for ordinal data: a comparison between standard and new approaches, p. 221-229 in Studies in Classification, Data Analysis, and Knowledge Organization. Advances in Statistical Models for Data Analysis. Editors: Morlini, I., Minerva, T. and Vichi, M. DOI 10.1007/978-3-319-17377-1