Research areas

Some of the research lines of the members of the group are listed below.

  • Big data

  • Biostatistics

  • Estimation of sets and statistics on manifolds

  • Functional data statistics

  • Genomic modeling

  • Kac-Rice formulas

  • Hermite - Wiener expansions

  • Level sets of random fields

  • Machine learning

  • Mathematical modeling in music

  • Modeling in telecommunications

  • Nonparametric statistics

  • Optimal stopping problems

  • Probability on networks and their applications

  • Particle systems

  • Random polynomials and random polynomial systems

  • Random waves

  • Robust estimation

  • Statistics of stochastic processes

  • Statistics applied to sports

  • Stochastic financial math

  • Supervised, unsupervised, and semi-supervised learning

Publications and pre-prints


  • Surface and length estimation based on Crofton's formula. C. Aaron, A. Cholaquidis, R. Fraiman. arXiv.

  • On semi-supervised learning. A. Cholaquidis, R. Fraiman, M. Sued. Test, 2020, 29(4), pp. 914–937. Link.

  • Sample design to monitor COVID-19 disease. D. Morales, M.J. Lombardía, R. Fraiman, J.A.C. Albertos. Boletín de Estadística e Investigacion Operativa, 2020, 36(2), pp. 153–171. Link.

  • Nonparametric detection for univariate and functional data. A. Cuevas, R. Fraiman. Journal of Statistical Planning and Inference, 2020. Link.

  • Sensitivity indices for output on a Riemannian manifold. R. Fraiman, F. Gamboa, L. Moreno. International Journal for Uncertainty Quantification, 2020, 10(4), pp. 297–314. Link.

  • Nonparametric regression based on discretely sampled curves. L. Forzani, R. Fraiman, P. Llop. Revstat Statistical Journal, 2020, 18(1), pp. 1–26. Link.

  • On the finiteness of the moments of the measure of level sets of random fields. D. Armentano, J-M. Azaïs, F. Dalmao, J. R. Léon, E. Mordecki. arXiv.

  • Studying the winding number of a Gaussian process: the real method. J-M. Azaïs, F. Dalmao, J. R. León. arXiv.

  • Large Deviation Principle for the Greedy Exploration Algorithm over Erdos-Rényi Graphs. P. Bermolen, V. Goicoechea, M. Jonckheere, E. Mordecki. arXiv.

  • Sequential Algorithms and Independent Sets Discovering on Large Sparse Random Graphs. P. Bermolen, M. Jonckheere, F. Larroca, M. Saenz. arXiv.

  • Large-Scale 802.11 Wireless Networks Data Analysis based on Graph Clustering. G. Capdehourat, P. Bermolen, M. Fiori, N. Frevenza, F. Larroca, G. Morales, C. Rattaro, G. Zunino.

  • Level set and density estimation on manifolds. A. Cholaquidis, R. Fraiman, L. Moreno. arXiv.

  • Set Estimation Under Biconvexity Restrictions. A. Cholaquidis, A. Cuevas. ESAIM: PS 24 770-788 (2020). Link.

  • On 3-dimensional Berry's model. F. Dalmao, A. Estrade, J. R. León. arXiv.

  • Convex envelopes on Trees. L. M. Del Pezzo, N. Frevenza, J. D. Rossi, Journal of Convex Analysis 27 (2020), No. 4, 1195--1218, Link.

  • Dirichlet-to-Neumann maps on Trees L. M. Del Pezzo, N. Frevenza, J. D. Rossi, Potential Analysis 53, (2020), Link.

  • Quasiconvex functions on regular trees. L. M. Del Pezzo, N. Frevenza, J. D. Rossi. arXiv

  • Sensitivity analysis in general metric spaces. F. Gamboa, T. Klein, A. Lagnoux, L. Moreno. arXiv

  • Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data. N. Hernández, R. Machado de Azevedo Neto, A. Duarte, G. Ost, R. Fraiman, A. Galves, C. D. Vargas. arXiv

  • Parameter Estimation for Discretely Observed Fractional Iterated Ornstein--Uhlenbeck Processes. J. Kalemkerian. Arxiv

  • Zero Black-Derman-Toy interest rate model. G. Krzyżanowski, E. Mordecki, A. Sosa. arXiv

  • Performance analysis of Zero Black-Derman-Toy interest rate model in catastrophic events: COVID-19 case study. G. Krzyżanowski, A. Sosa. arXiv

  • Two-sided optimal stopping for Lévy processes. E. Mordecki, F. Oliú Eguren. arXiv

  • QoS Provision in a Dynamic Channel Allocation Based on Admission Control Decisions. C. Rattaro, L. Aspirot, E. Mordecki, P. Belzarena, ACM Trans. Model. Perform. Eval. Comput. Syst. 5, (2020). Link.

  • Weighted lens depth: Some applications to supervised classification. A. Cholaquidis, R. Fraiman, F. Gamboa, L. Moreno. arXiv.

  • On a general definition of the functional linear model. J. R. Berrendero, A. Cholaquidis, A. Cuevas. arXiv.

  • Convex and quasiconvex functions in metric graphs. L. M. Del Pezzo, N. Frevenza, J. D. Rossi. arXiv.

The cover image belongs to the article Set estimation from reflected Brownian motion (Cholaquidis, Fraiman, Lugosi, Pateiro-López). These are data from the “Dunn Ranch Bison Tracking Project” that follow bison's trajectory in the Dunn Ranch Prairie, located in northwestern Missouri (USA).