Proyecto de investigación subvencionado por el Ministerio de Economía y Competitividad (código MTM2016-78751-P)

Investigadores principales: José Ramón Berrendero y Antonio Cuevas

Tesis doctorales dirigidas por miembros del proyecto (desde 2012)

En orden cronológico inverso:

Publicaciones (desde 2012)

Artículos aceptados para su publicación

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

  • Berrendero, J.R. and Cárcamo, J. (To appear). Linear components of quadratic classifiers. Advances in Data Analysis and Classification. Preprint | doi | comentario divulgativo

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

  • Chacón, J.E. (To appear). Mixture model modal clustering. Advances in Data Analysis and Classification. doi

2019

  • 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

  • Aneiros, G., Cao, R., Fraiman, R., Genest, C. and Vieu, P. (2019). Recent advances in functional data analysis and high-dimensional statistics. Journal of Multivariate Analysis, 170, 3-9. 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

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

2018

  • Berrendero, J.R., Cuevas, A. and Torrecilla, J.L. (2018). On the use of reproducing kernel Hilbert spaces in functional classification. Journal of the American Statistical Association, 113, 1210-1218. Preprint | Supplementary material | doi | Comentario divulgativo

  • Chacón, J.E. and Duong, T. (2018). Multivariate Kernel Smoothing and Its Applications. Chapman and Hall. Book website.

  • Cuevas, A. and Pateiro-López, B. (2018). Polynomial volume estimation and its applications. Journal of Statistical Planning and Inference, 196, 174-184. doi

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

  • Torrecilla, J.L. and Romo, J. (2018). Data learning from big data. Statistics and Probability Letters, 136, 15-19. doi

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. doi (open access).

  • 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

  • Amoruso, L., Ibáñez, A., Fonseca, B., Gadea, S., Sedeño, L., Sigman, M., García, A. M., Fraiman, R. and Fraiman, D. (2017). Variability in functional brain networks predicts expertise during action observation. NeuroImage, 146, 690-700. doi

  • 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

  • Fraiman, D., Fraiman, N. and Fraiman, R. (2017). Nonparametric statistics of dynamic networks with distinguishable nodes. TEST, 26, 546-573. 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 | The final publication is available at Springer via this doi link

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 | comentario divulgativo

  • Cholaquidis, A., Fraiman, R., Kalemkerian, J. and Llop, P. (2016). A nonlinear aggregation type classifier. Journal of Multivariate Analysis, 146, 269-281. 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 | comentario divulgativo

  • Fraiman, R., Gimenez, Y. and Svarc, M. (2016). Feature Selection for Functional Data. Journal of Multivariate Analysis, 146, 191-208. doi

  • 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.

  • 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

  • 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

  • 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. Enlace a las actas

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

  • 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.

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

  • 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.

  • 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.

  • Fraiman, R., Justel, A., Liu, R. and Llop, P. (2014). Detecting trends in time series of functional data: A study of Antarctic climate change. Canadian Journal of Statistics, 42, 597-609.

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.

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

  • 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. Preprint

  • 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

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

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.

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

  • 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

  • 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.