Published: Journal papers

  1. Elías, A., and Nagy, S. (2024). Statistical properties of partially observed integrated functional depths. TEST. To appear.
  2. Mendroš, E., and Nagy, S. (2024). Explicit bivariate simplicial depth. Journal of Multivariate Analysis. To appear.
  3. Liu, X., Liu, Y., Laketa, P., Nagy, S., and Chen Y. (2024). Exact and approximate computation of the scatter halfspace depth. Computational Statistics. To appear.
  4. Nagy, S., and Laketa, P. (2024). Theoretical properties of angular halfspace depth. Bernoulli. To appear.
  5. Bočinec, F., and Nagy, S. (2024). Conditions for equality in Anderson's theorem. Statistics & Probability Letters, to appear.
  6. Nagy, S., Demni, H., Buttarazzi, D., and Porzio, G. C. (2023). Theory of angular depth for classification of directional data. Advances in Data Analysis and Classification. To appear.
  7. Fojtík, V., Laketa, P., Mozharovskyi, P., and Nagy, S. (2023). On exact computation of Tukey depth central regions. Journal of Computational and Graphical Statistics. To appear.
  8. Pokorný, D., Laketa, P., and Nagy, S. (2024). Another look at halfspace depth: Flag halfspaces with applications. Journal of Nonparametric Statistics, 36(1), 165-181.
  9. Nagy, S. (2023). Simplicial depth and its median: Selected properties and limitations. Statistical Analysis and Data Mining, 16(4), 374-390.
  10. Laketa, P., and Nagy, S. (2023). Simplicial depth: Characterisation and reconstruction. Statistical Analysis and Data Mining, 16(4), 358-373.
  11. Balko, M., Scheucher, M., and Valtr, P. (2023). Tight bounds on the expected number of holes in random point sets. Random Structures and Algorithms, 62(1), 29-51.
  12. Laketa, P., Pokorný, D., and Nagy, S. (2022). Simple halfspace depth. Electronic Communications in Probability, 27, 1-12.
  13. Hendrych, F., and Nagy, S. (2022). A note on the convergence of lift zonoids of measures. Stat, 11(1), e453.
  14. Hlubinka, D., Kotík, L., and Šiman, M. (2022). Multivariate quantiles with both overall and directional probability interpretation. Scandinavian Journal of Statistics, 49(4), 1586-1604.
  15. Hallin, M., Hlubinka, D., and Hudecová, Š. (2022). Efficient fully distribution-free center-outward rank tests for multiple-output regression and MANOVA. Journal of the Americal Statistical Association. To appear.
  16. Ferraty, F., and Nagy. S. (2022). Scalar-on-function local linear regression and beyond. Biometrika, 109(2), 439-455.
  17. Patáková, Z., Tancer, M., and Wagner, U. (2022). Barycentric Cuts Through a Convex Body. Discrete & Computational Geometry, 68, 1133–1154.
  18. Laketa, P. and Nagy, S. (2022). Halfspace depth for general measures: The ray basis theorem and its consequences. Statistical Papers, 63, 849-883.
  19. Helander, S., Laketa, P., Ilmonen, P., Nagy, S., Van Bever, G., and Viitasaari, L. (2022). Integrated shape-sensitive functional metrics. Journal of Multivariate Analysis, 189, 104880.
  20. Balko, M., Chodounský, D., Hubička, J., Konečný, M., and Vena, L. (2022). Big Ramsey degrees of 3-uniform hypergraphs are finite. Combinatorica, 42, 659–672.
  21. Laketa, P., and Nagy, S. (2021). Reconstruction of atomic measures from their halfspace depth. Journal of Multivariate Analysis, 183, 104727.
  22. Dyckerhoff, R., and Mozharovskyi, P., and Nagy, S. (2021). Approximate computation of projection depths. Computational Statistics & Data Analysis, 157, 107166.
  23. Nagy, S., Helander, S., Van Bever, G., Viitasaari, L., and Ilmonen, P. (2021). Flexible integrated functional depths. Bernoulli, 27(1), 673-701.
  24. Rataj, J. (2021). Mean Euler characteristic of stationary random closed sets. Stochastic Processes and their Applications, 137, 252-271.
  25. Vencálek, O. and Hlubinka, D. (2021). A depth-based modification of the k-nearest neighbour method. Kybernetika, 57 (1), 15-37.
  26. Nagy, S., Dyckerhoff, R., and Mozharovskyi, P. (2020). Uniform convergence rates for the approximated halfspace and projection depth. Electronic Journal of Statistics, 14 (2), 3939-3975.
  27. Dvořák, J., Hudecová, Š., and Nagy, S. (2020). Clover plot: Versatile visualisation in nonparametric classification. Statistical Analysis and Data Mining, 13, 548-564.
  28. de Mesmay, A., Rieck, Y., Sedgwick, E., and Tancer, M. (2021). The unbearable hardness of unknotting. Advances in Mathematics, 381, 107648.
  29. Nagy, S. and Dvořák, J. (2021). Illumination depth. Journal of Computational and Graphical Statistics, 30 (1), 78-90.
  30. Nagy, S. (2021). Halfspace depth does not characterize probability distributions. Statistical Papers, 62, 1135-1139.
  31. Aichholzer, O., Balko, M., Hackl, T., Kynčl, J., Parada, I., Scheucher, M., Valtr P., and Vogtenhuber, B. (2020). A superlinear lower bound on the number of 5-holes. Journal of Combinatorial Theory, Series A, 173, 105236.
  32. Kynčl, J. (2020). Simple realizability of complete abstract topological graphs simplified. Discrete and Computational Geometry 64(1), 1-27.
  33. Balko, M., Scheucher, M., and Valtr, P. (2020). Holes and islands in random point sets. Random Structures & Algorithms, 63, 308-326.
  34. Balko, M., Bhore, S., Martínez-Sandoval, L., and Valtr, P. (2020). On Erdős–Szekeres-type problems for k-convex point sets. European Journal of Combinatorics, 89, 103157.
  35. Nagy, S. (2020). Scatter halfspace depth: Geometric insights. Applications of Mathematics, 65, 287–298.
  36. Nagy, S., Schütt, C., and Werner, E. (2019). Halfspace depth and floating body. Statistics Surveys, 13, 52-118.
  37. Nagy, S. (2019). Scatter halfspace depth for K-symmetric distributions. Statistics & Probability Letters, 149, 171-177.
  38. Nagy, S. and Ferraty, F. (2018). Data depth for measurable noisy random functions. Journal of Multivariate Analysis, 170, 95-114.

Published: Conference proceedings

  1. Laketa, P. and Nagy, S. (2023). Partial reconstruction of measures from halfspace depth. In: Grilli, L., Lupparelli, M., Rampichini, C., Rocco, E., Vichi, M., editors, Statistical Models and Methods for Data Science. CLADAG 2021. Studies in Classification, Data Analysis, and Knowledge Organization, pages 93-105. Springer, Cham.
  2. Nagy, S., Laketa, P., and Dyckerhoff, R. (2021). Angular halfspace depth: computation. In Giovanni C. Porzio, Carla Rampichini, and Chiara Bocci, editors, CLADAG 2021. Book of Abstracts and Short Papers, pages 169-172. Firenze University Press.
  3. Demni, H., Buttarazzi, D., Nagy, S., and Porzio, G. C. (2021). Angular halfspace depth: classification using spherical bagdistances. In Giovanni C. Porzio, Carla Rampichini, and Chiara Bocci, editors, CLADAG 2021. Book of Abstracts and Short Papers, pages 316-319. Firenze University Press.
  4. Laketa, P. and Nagy, S. (2021). Angular halfspace depth: central regions. In Giovanni C. Porzio, Carla Rampichini, and Chiara Bocci, editors, CLADAG 2021. Book of Abstracts and Short Papers, pages 356-359. Firenze University Press.
  5. Balko, M., Scheucher, M., and Valtr, P. (2021). Tight bounds on the expected number of holes in random point sets. 37th European Workshop on Computational Geometry (EuroCG 2021), 2:1-2:7.
  6. Balko, M., Scheucher, M., and Valtr, P. (2021). Tight bounds on the expected number of holes in random point sets. Extended Abstracts EuroComb 2021, 411-416.
  7. Nagy, S. (2020). Depth in infinite-dimensional spaces. In: Aneiros G., Horová I., Hušková M., Vieu P. (eds) Functional and High-Dimensional Statistics and Related Fields. IWFOS 2020, pages 187-195. Contributions to Statistics. Springer, Cham
  8. Nagy, S. (2020). The halfspace depth characterization problem. In: La Rocca M., Liseo B., Salmaso L. (eds) Nonparametric Statistics. ISNPS 2018, 379-389. Springer Proceedings in Mathematics & Statistics, vol 339. Springer, Cham.
  9. Nagy, S. and Dvořák, J. (2020). Robust depth-based inference in elliptical models. In: Balzano S., Porzio G. C., Salvatore R., Vistocco D., Vichi M. (eds) Statistical Learning and Modeling in Data Analysis - Methods and Applications. 129-137. Studies in Classification, Data Analysis and Knowledge Organization. Springer.
  10. Patáková, Z., Tancer, M., and Wagner, U. (2020). Barycentric cuts through a convex body. 36th International Symposium on Computational Geometry (SoCG 2020), 62:1-62:16.
  11. Balko, M., Scheucher, M., and Valtr, P. (2020). Holes and islands in random point sets. 36th International Symposium on Computational Geometry (SoCG 2020), 14:1-14:16.
  12. Nagy, S. and Dvořák, J. (2019). Illumination in depth analysis. In Giovanni C. Porzio, Francesca Greselin, and Simona Balzano, editors, CLADAG 2019. Book of Short Papers, 353-356. Università di Cassino e del Lazio Meridionale.
  13. 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.
  14. 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.
  15. 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.

Under review

  1. Wynne, G., and Nagy, S. (2021). Statistical depth meets machine learning: Kernel mean embeddings and depth in functional data analysis. Under review.
Copyright (c) 2023 Stanislav Nagy. All rights reserved.