Published

  1. Nagy, S. (2019). Halfspace depth does not characterize probability distributions. Statistical Papers. To appear.
  2. Nagy, S. and Dvořák, J. (2019). Illumination in depth analysis. In Givoanni C. Porzio, Francesca Greselin, and Simona Balzano, editors, CLADAG 2019. Book of Short Papers, 353-356. Università di Cassino e del Lazio Meridionale.
  3. Nagy, S. (2019). The halfspace depth characterization problem. Springer Proc. Math. Stat. To appear.
  4. Nagy, S., Schütt, C., and Werner, E. (2019). Halfspace depth and floating body. Statistics Surveys, 13, 52-118.
  5. Nagy, S. (2019). Scatter halfspace depth for K-symmetric distributions. Statistics & Probability Letters, 149, 171-177.
  6. Fulek, R. and Kyncl, J. (2019). Z2-genus of graphs and minimum rank of partial symmetric matrices. Proceedings of the 35th International Symposium on Computational Geometry (SoCG 2019), Leibniz International Proceedings in Informatics (LIPIcs) 129, 39:1-39:16, Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
  7. de Mesmay, A., Rieck, Y., Sedgwick, E., and Tancer, M. (2019). The unbearable hardness of unknotting. Proceedings of the 35th International Symposium on Computational Geometry (SoCG 2019), Leibniz International Proceedings in Informatics (LIPIcs) 129, 49:1--49:19, Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
  8. Balko, M., Bhore, S., Martínez-Sandoval, L., and Valtr, P. (2019). On Erdős–Szekeres-type problems for k-convex point sets. In the Proceedings of the 30th International Workshop on Combinatorial Algorithms (IWOCA 2019), Lecture Notes in Computer Science, vol 11638. Springer, Cham, 35–47.
  9. Nagy, S. and Ferraty, F. (2018). Data depth for measurable noisy random functions. Journal of Multivariate Analysis, 170, 95-114.

Under review

  1. Nagy, S. (2019). Scatter halfspace depth: Geometric insights.
  2. Nagy, S., Helander, S., Van Bever, G., Viitasaari, L., and Ilmonen, P. (2019). Adaptive integrated functional depths.
  3. Nagy, S., Dyckerhoff, R., and Mozharovskyi, P. (2019). Uniform convergence rates for the approximated halfspace and projection depth. arXiv:1910.05956
  4. Ferraty, F. and Nagy, S. (2019). Scalar-on-function local linear regression and beyond. arXiv:1907.08074
  5. Nagy, S. and Dvořák, J. (2019). Illumination depth. arXiv:1905.04119
  6. Hlubinka, D., Kotík, L., and Šiman, M. (2019). Multivariate quantiles with both overall and directional probability interpretation.
  7. Vencálek, O. and Hlubinka, D. (2019). A depth-based modification of the k-nearest neighbour method.
  8. Kyncl, J. (2019). Simple realizability of complete abstract topological graphs simplified. arXiv:1608.05867.
  9. Balko, M., Scheucher, M., and Valtr, P. (2019). Holes and islands in random point sets.
  10. Balko, M., Bhore, S., Martínez-Sandoval, L., and Valtr, P. (2019). On Erdős–Szekeres-type problems for k-convex point sets (full version).
  11. Paták, P. and Tancer, M. (2018). Embeddings of k-complexes into 2k-manifolds. arXiv:1904.02404