Research project granted by the Spanish Ministry of Science and Innovation (grant code PID2019-109387GB-I00)

Main researchers: José R. Berrendero and Antonio Cuevas


Members

(*) Members of the collaboration group / Miembros del equipo de trabajo

Ph. D. Thesis supervised by researchers of the project (since 2012)

In reverse chronological order:

Publications (since 2012)

Articles accepted for publication

  • Barroso, M., Alaíz, C. M., Torrecilla, J. L. and Fernández, Á. (To appear). Functional Diffusion Maps. Statistics and Computing.
    Preprint

  • Chacón, J.E. and Fernández-Serrano, J. (To appear). Bump hunting through density curvature features. TEST.
    Preprint

  • Ramos-Carreño, C., Torrecilla, J. L., Carbajo-Berrocal, M., Marcos, P. and Suárez, A. (To appear). scikit-fda: a Python package for functional data analysis. Journal of Statistical Software.
    Preprint

2023

  • Berrendero, J.R., Bueno-Larraz, B. and Cuevas, A. (2023). On functional logistic regression: some conceptual issues. TEST, 32, 321-349.
    Paper (open access)

  • Chacón, J.E. and Rastrojo, A.I. (2023). Minimum adjusted Rand index for two clusterings of a given size. Advances in Data Analysis and Classification, 17, 125-133.
    Paper (open access)

  • Ramos-Carreño, C. and Torrecilla, J.L. (2023). dcor: Distance correlation and energy statistics in Python. SoftwareX, 22, 101326.
    Paper (open access)

2022

  • Baíllo, A., Cárcamo, J. and Mora-Corral, C. (2022). Extreme points of Lorenz and ROC curves with applications to inequality analysis, Journal of Mathematical Analysis and Applications, 514, 2, 126335.
    Paper (open access)

  • Baíllo, A. and Chacón, J.E. (2022). A new selection criterion for statistical home range estimation. Journal of Applied Statistics, 49, 722-737.
    doi

  • Cao, R. and Chacón, J.E. (2022). Introduction to the special issue on Data Science for COVID-19. Journal of Nonparametric Statistics, 34, 555-569.
    Paper (free access)

  • Ramos-Carreño, C., Torrecilla, J.L., Hong, Y. and Suárez, A. (2022). scikit-fda: Computational Tools for Machine Learning with Functional Data. In 2022 IEEE 34rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 213-218). IEEE.
    Preprint

  • Ramos-Carreño, C., Torrecilla, J. L. and Suárez, A. (2022). Classification of Functional Data: A Comparative Study, In 2022 IEEE 21st International Conference on Machine Learning and Applications (ICMLA) (pp. 866-871). IEEE.
    Preprint | doi

2021

  • Baíllo, A. and Chacón, J.E. (2021). Statistical outline of animal home ranges: An application of set estimation. In Data Science: Theory and Applications (A.S.R. Srinivasa Rao and C.R. Rao, eds.). Handbook of Statistics, 44, 3–37.
    doi

  • Baíllo, A. and Grané, A. (2021). Subsampling and aggregation: A solution to the scalability problem in distance-based prediction for mixed-type data. Mathematics, 9, 2247.
    Paper (open access)

  • Chacón, J.E. (2021). A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation. British Journal of Mathematical and Statistical Psychology, 74, 203–231.
    Preprint | doi

  • Chacón, J.E. (2021). Explicit agreement extremes for a 2×2 table with given marginals. Journal of Classification, 38, 257–263.
    Preprint | doi | expository note (Spanish)

2020

  • Berrendero, J.R., Bueno-Larraz, B. and Cuevas, A. (2020). On Mahalanobis distance in functional settings. Journal of Machine Learning Research, 21, 1-33.
    Paper (open access) | expository note (Spanish)

  • Cárcamo, J., Cuevas, A., Rodríguez, L.A. (2020). Directional differentiability for supremum-type functionals: statistical applications. Bernoulli, 26, 2143-2175.
    Preprint | doi

  • Casa, A., Chacón, J.E. and Menardi, G. (2020). Modal clustering asymptotics with applications to bandwidth selection. Electronic Journal of Statistics, 14, 835-856.
    Paper (open access)

  • Chacón, J.E. (2020). The Modal Age of Statistics. International Statistical Review, 88, 122-141.
    Preprint | doi

  • Cholaquidis, A. and Cuevas, A. (2020). Set estimation under biconvexity restrictions. ESAIM: Probability and Statistics, 24, 770-788.
    Preprint

  • Cuevas, A. and Fraiman, R. (2020). Nonparametric detection for univariate and functional data. Journal of Statistical Planning and Inference, 209, 12-26.
    doi

  • Torrecilla, J.L., Ramos-Carreño, C., Sánchez-Montañés, M. and Suárez, A. (2020). Optimal classification of Gaussian processes in homo- and heteroscedastic settings. Statistics and Computing, 30, 1091–1111.
    doi

2019

  • Baíllo, A., Cárcamo, J. and Getman, K. (2019). New distance measures for classifying X-ray astronomy data into stellar classes. Advances in Data Analysis and Classification, 13, 531-557.
    Preprint | doi

  • Berrendero, J.R., Bueno-Larraz, B. and Cuevas, A. (2019). An RKHS model for variable selection in functional linear regression. Journal of Multivariate Analysis, 170, 22-45.
    Preprint | doi

  • Berrendero, J.R. and Cárcamo, J. (2019). Linear components of quadratic classifiers. Advances in Data Analysis and Classification, 13, 347-377.
    Preprint | doi | expository note (Spanish)

  • Chacón, J.E. (2019). Mixture model modal clustering. Advances in Data Analysis and Classification, 13, 379-404.
    doi

  • Torrecilla, J.L., Quijano-Sánchez, L., Liberatore, F., López-Ossorio, J.J., and González-Álvarez, J.L. (2019). Evolution and study of a copycat effect in intimate partner homicides: A lesson from Spanish femicides. PloS one, 14(6):e0217914.
    Paper (open access)

2018

2017

  • Aaron, C., Cholaquidis, A. and Cuevas, A. (2017). Detection of low dimensionality and data denoising via set estimation techniques. Electronic Journal of Statistics, 11, 4596-4628.
    Paper (open access).

  • Barba, I., Miró-Casas, E., Torrecilla, J.L., Pladevall, E., Tejedor, S., Sebastián-Pérez, R., Ruiz-Meana, M., Berrendero, J.R., Cuevas, A. and García-Dorado, D. (2017). High Fat Diet Induces Metabolic Changes and Reduces Oxidative Stress in Female Mouse Hearts. Journal of Nutritional Biochemistry, 40, 187–193.
    Preprint | doi

  • Cárcamo, J. (2017). Maps preserving moment sequences. Journal of Theoretical Probability, 30, 212-232.
    doi

  • Cárcamo, J. (2017). Integrated empirical processes in Lp with applications to estimate probability metrics. Bernoulli, 23, 3412-3436.
    Preprint | doi

  • Cholaquidis, A., Forzani, L., Llop, P. and Moreno, L. (2017). On the classification problem for Poisson Point Processes. Journal of Multivariate Analysis, 153, 1-15.
    Preprint | doi

  • Cuevas, A., Cholaquidis, A. and Fraiman, R. (2017). On visual distances for spectrum-type functional data. Advances in Data Analysis and Classification, 11, 5-24.
    Preprint | doi

  • Muelas, D., López de Vergara, J.E., Berrendero, J.R., Ramos, J. and Aracil, J. (2017). Facing network management challenges with functional data analysis: techniques and opportunities. Mobile Networks and Applications, 22, 1124-1136.
    Preprint | doi

2016

  • Berrendero, J.R., Cuevas, A. and Pateiro-López, B. (2016). Shape classification based on interpoint distance distributions. Journal of Multivariate Analysis, 146, 237-247.
    Preprint | doi

  • Berrendero, J.R., Cuevas, A. and Torrecilla, J.L. (2016). The mRMR variable selection method: a comparative study for functional data. Journal of Statistical Computation and Simulation, 86, 891-907.
    Preprint | Supplementary material | doi

  • Berrendero, J.R., Cuevas, A. and Torrecilla, J.L. (2016). Variable selection in functional data classification: a maxima-hunting proposal. Statistica Sinica, 26, 619-638.
    Preprint | doi | expository note (Spanish)

  • Torrecilla, J.L. and Suárez, A. (2016). Feature selection in functional data classification with recursive maxima hunting. Advances in Neural Information Processing Systems (NIPS 2016 proceedings), 4835-4843.
    Preprint

2015

  • Baíllo, A., Cárcamo, J. and Nieto, S. (2015). A test for convex dominance with respect to the exponential class based on an L1 distance. IEEE Transactions on Reliability, 64, 71–82.
    doi

  • Berrendero, J.R. (2015). Simulación e inferencia estadística. La Gaceta de la RSME, 18, 45-65.
    Preprint

  • Chacón, J.E. (2015). A population background for nonparametric density-based clustering. Statistical Science, 30, 518-532.
    Preprint | doi

  • Chacón, J.E. and Duong, T. (2015) Efficient recursive algorithms for functionals based on higher order derivatives of the multivariate Gaussian density. Statistics and Computing, 25, 959-974.
    Preprint | doi

  • Muelas, D., López de Vergara, J.E. and Berrendero, J.R. (2015). Functional Data Analysis: A step forward in Network Management. Proceedings of IFIP/IEEE International Symposium on Integrated Network Management, IM’2015 (ISBN: 978-3-901882-76-0), 882-885.
    Preprint | doi

  • Muelas, D., López de Vergara, J.E., Berrendero, J.R. and Aracil, J. (2015). Análisis funcional para gestión de red: técnicas, retos y oportunidades. Actas de las XII Jornadas de Ingeniería Telemática, Jitel’2015 (ISBN: 978-84-606-8609-5), 197-204.
    Link to the workshop proceedings

2014

  • Berrendero, J.R., Cholaquidis, A., Cuevas, A. and Fraiman, R. (2014). A geometrically motivated parametric model in manifold estimation. Statistics, 48, 983-1004.
    Preprint | doi

  • Chacón, J.E., Monfort, P. and Tenreiro, C. (2014). Fourier methods for smooth distribution function estimation. Statistics and Probability Letters, 84, 223-230.
    Preprint | doi

  • Chacón, J.E. and Monfort, P. (2014). A comparison of bandwidth selectors for mean shift clustering, in Theoretical and Applied Issues in Statistics and Demography (C. H. Skiadas, Ed), ISAST.
    Preprint

  • Cholaquidis, A., Cuevas, A. and Fraiman, R. (2014). On Poincaré cone property. The Annals of Statistics, 42, 255-284.
    Paper (open access)

  • Cuevas, A. (2014). A partial overview of the theory of statistics with functional data. Journal of Statistical Planning and Inference, 147, 1-23.
    doi

  • Cuevas, A. (2014). Different perspectives on Object Oriented Data Analysis. Biometrical Journal 56, 754-757. This is a comment on the paper “An Overview of Object Oriented Data Analysis” by J.S. Marron and A.M. Alonso.
    Paper (open access)

  • Cuevas, A., Pateiro-López, B. and Llop, P. (2014). On the estimation of the medial axis and inner parallel body. Journal of Multivariate Analysis, 129, 171-185.
    doi

2013

  • Baíllo, A., Martínez-Muñoz, L. and Mellado, M. (2013). Homogeneity tests for Michaelis-Menten curves with application to fluorescence resonance energy transfer data. Journal of Biological Systems, 21, 1350017.
    doi

  • Berrendero, J.R. and Cárcamo, J. (2013). Reply to Baker (2013), letter to the editor, The American Statistician, 67, 65.
    doi

  • Chacón, J.E. and Duong, T. (2013). Data-driven density derivative estimation, with applications to nonparametric clustering and bump hunting. Electronic Journal of Statistics, 7, 499-532.
    Paper (open access)

  • Chacón, J.E. and Tenreiro, C. (2013). Data-based choice of the number of pilot stages for plug-in bandwidth selection. Communications in Statistics, Theory and Methods., 42, 2200-2214.
    Preprint | doi

  • Cuevas, A., Fraiman, R. and Györfy, L. (2013). Towards a universally consistent estimator of the Minkowski content. ESAIM: Probability and Statistics, 17, 359-369.
    doi

2012

  • Barroso, R., Muñoz, L. M., Barrondo, S., Vega, B., Holgado, B. L., Lucas, P., Baíllo, A., Salles, J., Rodríguez-Frade, J. M. and Mellado, M. (2012). EBI2 regulates CXCL13-mediated responses by heterodimerization with CXCR5. The FASEB Journal, 26, 4841-4854.
    doi

  • Berrendero, J.R. and Cárcamo, J. (2012). The tangent classifier. The American Statistician, 66, 185-194.
    Preprint | doi | expository note (Spanish)

  • Berrendero, J.R. and Cárcamo, J. (2012). Tests for stochastic orders and mean order statistics. Communications in Statistics -Theory and Methods, 41, 1497-1509.
    Preprint | doi

  • Berrendero, J.R., Cuevas, A. and Pateiro-López, B. (2012). Testing uniformity for the case of a planar unknown support. Canadian Journal of Statistics, 40, 378-395.
    Preprint | doi

  • Berrendero, J.R., Cuevas, A. and Pateiro-López, B. (2012). A multivariate uniformity test for the case of unknown support. Statistics and Computing, 22, 259-271.
    Preprint | doi

  • Chacón, J. E. and Tenreiro, C. (2012). Exact and asymptotically optimal bandwidths for kernel estimation of density functionals. Methodology and Computing in Applied Probability, 14, 523-548.
    Preprint | doi

  • Cuevas, A., Fraiman, R. and Pateiro-López, B. (2012). On statistical properties of sets fulfilling rolling-type conditions. Advances in Applied Probability, 44, 311-329.
    doi

Selected publications by members of the collaboration group

  • Aaron, C. and Cholaquidis, A. (2020). On boundary detection. Annales de l’Institut Henri Poincaré - Probabilités et Statistiques.
    Preprint | doi

  • Aaron, C., Cholaquidis, A. and Fraiman, R. (2017). A generalization of the maximal-spacings in several dimensions and a convexity test. Extremes, 20, 605-634.
    Preprint | doi

  • Aaron, C., Cholaquidis, A., Fraiman, R. and Ghattas, B. (2019). Multivariate and functional robust fusion methods for structured Big Data. Journal of Multivariate Analysis, 170, 149-161. doi

  • Bueno-Larraz, B. and Klepsch, J. (2019). Variable selection for the prediction of C[0,1]-valued AR processes using RKHS. Technometrics, 61, 139-153.
    Preprint | doi

  • Cholaquidis, A., Fraiman, R., Lugosi, G. and Pateiro-López, B. (2016). Set estimation from reflected Brownian motion. Journal of the Royal Statistical Society: Series B, 78, 1057-1078.
    doi | expository note (Spanish)

  • Cholaquidis, A., Fraiman, R., Mordecki, E. and Papalardo, C. (2021). Level sets and drift estimation for reflected Brownian motion with drift. Statistica Sinica, 31, 29-51.
    Preprint | doi

  • Cholaquidis, A., Fraiman, R. and Sued, M. (2020). On semi-supervised learning. Test, 29, 914-937.
    Preprint | doi

  • Fraiman, D. and Fraiman, R. (2018). An ANOVA approach for statistical comparisons of brain networks. Scientific Reports, 8, article number 4746.
    doi

  • Fraiman, D., Fraiman, N. and Fraiman, R. (2017). Nonparametric statistics of dynamic networks with distinguishable nodes. TEST, 26, 546-573.
    Preprint | doi

  • Fraiman, R., Gamboa, F. and Moreno, L. (2019). Connecting pairwise geodesic spheres by depth: DCOPS. Journal of Multivariate Analysis, 169, 81-94.
    doi

  • González, M., Minuesa, C. and del Puerto, I. (2017). Minimum disparity estimation in controlled branching processes. Electronic Journal of Statistics, 11, 295-325.
    Paper (open access)

  • González, M., Minuesa, C. and del Puerto, I. (2016). Maximum likelihood estimation and Expectation-Maximization algorithm for Controlled Branching Processes. Computational Statistics and Data Analysis, 93, 209-227.
    doi

  • González, M., Minuesa, C., del Puerto, I. and Vidyashankar, A. N. (2021). Robust estimation in controlled branching processes: Bayesian estimators via disparities. Bayesian Analysis, 16, 1009-1037.
    doi