List of Publications

  1. Wynne, G., and Nagy, S. (2025). Statistical depth meets Machine Learning: Kernel Mean Embeddings and depth in functional data analysis. International Statistical Review. To appear.
  2. Mendroš, E., and Nagy, S. (2025). k-hull depth: In between simplicial and halfspace depth. Statistical Papers. To appear.
  3. Elías, A., and Nagy, S. (2024). Statistical properties of partially observed integrated functional depths. TEST. To appear.
  4. Mendroš, E., and Nagy, S. (2025). Explicit bivariate simplicial depth. Journal of Multivariate Analysis, 205, 105375.
  5. Liu, X., Liu, Y., Laketa, P., Nagy, S., and Chen Y. (2024). Exact and approximate computation of the scatter halfspace depth. Computational Statistics, 40, 547-572.
  6. Nagy, S., and Laketa, P. (2025). Theoretical properties of angular halfspace depth. Bernoulli 31(2), 1007-1031.
  7. Bočinec, F., and Nagy, S. (2024). Conditions for equality in Anderson's theorem. Statistics & Probability Letters, 209, 110094.
  8. 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, 18, 627-662.
  9. Fojtík, V., Laketa, P., Mozharovskyi, P., and Nagy, S. (2023). On exact computation of Tukey depth central regions. Journal of Computational and Graphical Statistics, 33(2), 699-713.
  10. 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.
  11. Nagy, S. (2023). Simplicial depth and its median: Selected properties and limitations. Statistical Analysis and Data Mining, 16(4), 374-390.
  12. Laketa, P., and Nagy, S. (2023). Simplicial depth: Characterisation and reconstruction. Statistical Analysis and Data Mining, 16(4), 358-373.
  13. Laketa, P., Pokorný, D., and Nagy, S. (2022). Simple halfspace depth. Electronic Communications in Probability, 27, 1-12.
  14. Hendrych, F., and Nagy, S. (2022). A note on the convergence of lift zonoids of measures. Stat, 11(1), e453.
  15. Ferraty, F., and Nagy. S. (2022). Scalar-on-function local linear regression and beyond. Biometrika, 109(2), 439-455.
  16. Laketa, P. and Nagy, S. (2022). Halfspace depth for general measures: The ray basis theorem and its consequences. Statistical Papers, 63, 849-883.
  17. 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.
  18. Laketa, P., and Nagy, S. (2021). Reconstruction of atomic measures from their halfspace depth. Journal of Multivariate Analysis, 183, 104727.
  19. Dyckerhoff, R., and Mozharovskyi, P., and Nagy, S. (2021). Approximate computation of projection depths. Computational Statistics & Data Analysis, 157, 107166.
  20. Nagy, S., Helander, S., Van Bever, G., Viitasaari, L., and Ilmonen, P. (2021). Flexible integrated functional depths. Bernoulli, 27(1), 673-701.
  21. 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.
  22. Dvořák, J., Hudecová, Š., and Nagy, S. (2020). Clover plot: Versatile visualisation in nonparametric classification. Statistical Analysis and Data Mining, 13, 548-564.
  23. Nagy, S. and Dvořák, J. (2021). Illumination depth. Journal of Computational and Graphical Statistics, 30 (1), 78-90.
  24. Nagy, S. (2021). Halfspace depth does not characterize probability distributions. Statistical Papers, 62, 1135-1139.
  25. Nagy, S. (2020). Scatter halfspace depth: Geometric insights. Applications of Mathematics, 65, 287–298.
  26. Nagy, S., Schütt, C., and Werner, E. (2019). Halfspace depth and floating body. Statistics Surveys, 13, 52-118.
  27. Nagy, S. (2019). Scatter halfspace depth for K-symmetric distributions. Statistics & Probability Letters, 149, 171-177.
  28. Nagy, S. and Ferraty, F. (2018). Data depth for measurable noisy random functions. Journal of Multivariate Analysis, 170, 95-114.
  29. Gijbels, I. and Nagy, S. (2017). On a general definition of depth for functional data. Statistical Science, 32 (4), 630-639.
  30. Nagy, S., Gijbels, I., and Hlubinka, D. (2017). Depth-based recognition of shape outlying functions. Journal of Computational and Graphical Statistics, 26 (4), 883-893.
  31. Nagy, S. and Gijbels, I. (2017). Law of large numbers for discretely observed random functions. Journal of the Korean Statistical Society, 46 (4), 562-572.
  32. Nagy, S. (2017). Monotonicity properties of spatial depth. Statistics & Probability Letters, 129, 373-378.
  33. Nagy, S. (2017). Integrated depth for measurable functions and sets. Statistics & Probability Letters, 123, 165-170.
  34. Nagy, S., Gijbels, I., Omelka, M., and Hlubinka, D. (2016). Integrated depth for functional data: statistical properties and consistency. ESAIM: Probability and Statistics, 20, 95-130.
  35. Nagy, S., Gijbels, I., and Hlubinka, D. (2016). Weak convergence of discretely observed functional data with applications. Journal of Multivariate Analysis, 146, 46-62.
  36. Gijbels, I. and Nagy, S. (2016). On smoothness of Tukey depth contours. Statistics, 50 (5), 1075-1085.
  37. Nagy, S. (2015). Consistency of h-mode depth. Journal of Statistical Planning and Inference, 165, 91-103.
  38. Gijbels, I. and Nagy, S. (2015). Consistency of non-integrated depths for functional data. Journal of Multivariate Analysis, 140, 259-282.
  39. Hlubinka, D., Gijbels, I., Omelka, M., and Nagy, S. (2015). Integrated data depth for smooth functions and its application in supervised classification. Computational Statistics, 30 (4), 1011-1031.

Chapters in Books

  1. Hernández, N. and Nagy, S. (2025). The common support function with applications. In: Bongiorno, E., Goia, A., Aneiros G., Hušková M. (eds) New Trends in Functional Statistics and Related Fields. IWFOS 2025. To appear.
  2. Bočinec, F., Mendroš, E., and Nagy, S. (2025). A comparison of band-based approaches to functional depth. In: Bongiorno, E., Goia, A., Aneiros G., Hušková M. (eds) New Trends in Functional Statistics and Related Fields. IWFOS 2025. To appear.
  3. Nagy, S. (2025). Interpretable functional boxplots. In: Bongiorno, E., Goia, A., Aneiros G., Hušková M. (eds) New Trends in Functional Statistics and Related Fields. IWFOS 2025. To appear.
  4. Mendroš, E. and Nagy, S. (2025). The spherical depth for functional data. In: Bongiorno, E., Goia, A., Aneiros G., Hušková M. (eds) New Trends in Functional Statistics and Related Fields. IWFOS 2025. To appear.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  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. To appear. Studies in Classification, Data Analysis and Knowledge Organization. Springer.
  10. 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
  11. Nagy, S. (2020). The halfspace depth characterization problem. In La Rocca M., Liseo B., Salmaso L., editors, Nonparametric Statistics. ISNPS 2018, 379-389. Springer Proceedings in Mathematics & Statistics, vol 339. Springer, Cham. With supplementary material.
  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, pages 353-356. Università di Cassino e del Lazio Meridionale.
  13. Nagy, S. (2017). An overview of consistency results for depth functionals. In Germán Aneiros, Enea G. Bongiorno, Ricardo Cao, and Philippe Vieu, editors, Functional statistics and related fields, pages 189-196. Springer.
  14. Nagy, S., Gijbels, I., Hlubinka, D., and Omelka, M. (2016). Functional data depth. Oberwolfach Report No. 12/2016, pages 24-26.
  15. Nagy, S. (2014). On the consistency of depth functionals. In Enea G. Bongiorno, Ernesto Salinelli, Aldo Goia, and Philippe Vieu, editors, Contributions in infinite-dimensional statistics and related topics, pages 197-202. Società Editrice Esculapio.
  16. Nagy, S. (2013). Coordinatewise characteristics of functional data. In Hana Vojáčková, editor, Proceedings 31th Int. Conf. Mathematical Methods in Economics 2013, Jihlava, Czech Republic, pages 655-660 (Part II.). College of Polytechnics Jihlava.
  17. Nagy, S. (2013). Depth for vector-valued functions. In J. Šafránková and J. Pavlů, editors, WDS'13 Proceedings of Contributed Papers, pages 85-90 (Part I.). Prague, Matfyzpress.
  18. Nagy, S. (2012). Nonparametric classification of noisy functions. In Arnošt Komárek and Stanislav Nagy, editors, Proceedings of the 27th International Workshop on Statistical Modelling, pages 234-239 (Part I.).

Books as Editor

  1. Nagy, S., editor (2015). Proceedings of the 19th European Young Statisticians Meeting, Prague.
  2. Komárek, A. and Nagy, S., editors (2012). Proceedings of the 27th International Workshop on Statistical Modelling, Vol. 1, Vol. 2, Prague.

Software

  1. Pokotylo, O., Mozharovskyi, P., Dyckerhoff, R., and Nagy, S. (2017). ddalpha: Depth-Based Classification and Calculation of Data Depth. R package version 1.3.1. Available at CRAN
Copyright (c) 2023 Stanislav Nagy. All rights reserved.