Beatriz Pateiro López

Área de Estatística e Investigación Operativa

Universidade de Santiago de Compostela
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  • Beatriz Pateiro López
  • Rúa Lope Gómez de Marzoa s/n
    15782, Santiago de Compostela
  • beatriz.pateiro@usc.es
  • +34 881813185
  • http://eamo.usc.es/pub/pateiro
About me

I received my PhD in Statistics and Operations Research at the University of Santiago de Compostela in 2008, with a dissertation entitled "Set estimation under convexity type restrictions". After completing my PhD, I worked as a postdoctoral researcher (Ángeles Alvariño postdoctoral fellowship). Currently I am Associate Professor at the Department of Statistics and Operations Research (University of Santiago de Compostela).
My research interests include set estimation and computational statistics.

Publications

  •    Gómez-Casares, Ignacio, Pateiro-López, B., González-Rodríguez, B., González-Díaz, J. (2026 (To appear)). ASML: An R Package for Algorithm Selection with Machine Learning. The R journal

      Cholaquidis, A., Cuevas, A., Pateiro-López, B. (2026). On consistent estimation of dimension values. Journal of Multivariate Analysis. 214, 105591

      González-Díaz, J., Ghaddar, B., Gómez-Casares, Ignacio, González-Rodríguez, B., Pateiro-López, B. (2025). Learning in Reformulation-Linearization Technique-Based Spatial Branching: Limitations of Strong Branching Imitation. Informs Journal on Computing

      Cholaquidis, A., Moreno, L., Pateiro-López, B. (2025). On standardness: estimation of the standardness constant and decidability aspects. Journal of Nonparametric Statistics, 1-25

      Cholaquidis, A., Fraiman, R., Moreno, L., Pateiro-López, B. (2024). Statistical analysis of measures of non-convexity. TEST. 33, 180-203

      Ghaddar, B., Gómez-Casares, Ignacio, González-Díaz, J., González-Rodríguez, B., Pateiro-López, B., Rodríguez Ballesteros, S. (2023). Learning for Spatial Branching: An Algorithm Selection Approach. Informs Journal on Computing. 35, 1024-1043

      Arias Castro, E., Pateiro-López, B., Rodríguez-Casal, A. (2019). Minimax Estimation of the Volume of a Set under the Rolling Ball Condition. Journal of the American Statistical Association-Theory and Methods. 114, 1162-1173

      Pichel, Juan C., Pateiro-López, B. (2019). Sparse Matrix Classification on Imbalanced Datasets using Convolutional Neural Networks. IEEE Access. 7, 82377-82389

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

      Pichel, Juan C., Pateiro-López, B. (2018). A New Approach for Sparse Matrix Classification Based on Deep Learning Techniques. IEEE International Conference on Cluster Computing, 46-54

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

      Cholaquidis, A., Fraiman, R., Lugosi, G., Pateiro-López, B. (2016). Set estimation from reflected Brownian motion. Journal of the Royal Statistical Society: Series B. 78(5), 1057-1078

      Lafarge, T., Pateiro-López, B., Possolo, A., Dunkers, J.P. (2014). R Implementation of a Polyhedral Approximation to a 3D Set of Points Using the alpha-Shape. Journal of Statistical Software. 56(4), 1-18

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

      Capinha, C., Pateiro-López, B. (2014). Predicting species distributions in new areas or time periods with alpha-shapes. Ecological Informatics. 24, 231-237

      Pateiro-López, B., Rodríguez-Casal, A. (2013). Recovering the shape of a point cloud in the plane. TEST. 22, 19-45

      Berrendero, J.R., Cuevas, A., Pateiro-López, B. (2012). A multivariate uniformity test for the case of unknown support. Statistics and Computing. 22(1), 259-271

      Cuevas, A., Fraiman, R., Pateiro-López, B. (2012). On statistical properties of sets fulfilling rolling-type conditions. Advances in Applied Probability. 44(2), 311-329

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

      Fraiman, R., Pateiro-López, B. (2012). Quantiles for finite and infinite dimensional data. Journal of Multivariate Analysis. 108, 1-14

      Fraiman, R., Pateiro-López, B. (2011). Functional Quantiles. Recent Advances in Functional Data Analysis and Related Topics, 123-129

      Pateiro-López, B., Rodríguez-Casal, A. (2010). Generalizing the Convex Hull of a Sample: The R Package alphahull. Journal of Statistical Software. 34 (5), 1-28

      Pateiro-López, B., Rodríguez-Casal, A. (2009). Surface area estimation under convexity type assumptions. Journal of Nonparametric Statistics. 21 (6), 729-741

      Pateiro-López, B., Rodríguez-Casal, A. (2008). Length and surface area estimation under smoothness restrictions. Advances in Applied Probability. 40 (2), 348-358

      Pateiro-López, B.(2008). Set estimation under convexity type restrictions. PhD Thesis

      Pateiro-López, B., González-Manteiga, W. (2006). Multivariate Partially Linear Models. Statistics & Probability Letters. 76, Issue 14, 1543-1549


Software

R packages
  • alphahull: Generalization of the convex hull of a sample of points in the plane  

    Authors: Beatriz Pateiro-Lopez [aut, cre], Alberto Rodriguez-Casal, [aut].
    Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.

  • alphashape3d: 3D alpha-shape for the reconstruction of 3D sets from a point cloud   

    Authors: Thomas Lafarge [aut, cre], Beatriz Pateiro-López [aut].
    The package alphashape3d presents the implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. This geometric structure generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, the package alphashape3d provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.

  • ASML: Algorithm Portfolio Selection with Machine Learning  

    Authors: Brais González-Rodríguez [aut, cre], Ignacio Gómez-Casares [aut], Beatriz Pateiro-López [aut], Julio González-Díaz [aut], María Caseiro-Arias [ctb], Antonio Fariña-Elorza [ctb], Manuel Timiraos-López [ctb]
    A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023)

Projects

Complete information on the projects is available through the web site of the department