List of Publications

  1. Gijbels, I. and Nagy, S. (2017). On a general definition of depth for functional data. Statistical Science (to appear).
  2. Nagy, S., Gijbels, I., and Hlubinka, D. (2017). Depth-based recognition of shape outlying functions. Journal of Computational and Graphical Statistics (to appear).
  3. 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.
  4. Nagy, S. (2017). Monotonicity properties of spatial depth. Statistics & Probability Letters, 129, 373-378.
  5. Nagy, S. (2017). Integrated depth for measurable functions and sets. Statistics & Probability Letters, 123, 165-170.
  6. 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.
  7. Nagy, S., Gijbels, I., and Hlubinka, D. (2016). Weak convergence of discretely observed functional data with applications. Journal of Multivariate Analysis, 146, 46-62. Special Issue on Statistical Models and Methods for High or Infinite Dimensional Spaces.
  8. Gijbels, I. and Nagy, S. (2016). On smoothness of Tukey depth contours. Statistics, 50 (5), 1075-1085.
  9. Nagy, S. (2015). Consistency of h-mode depth. Journal of Statistical Planning and Inference, 165, 91-103.
  10. Gijbels, I. and Nagy, S. (2015). Consistency of non-integrated depths for functional data. Journal of Multivariate Analysis, 140, 259-282.
  11. 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. 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.
  2. Nagy, S., Gijbels, I., Hlubinka, D., and Omelka, M. (2016). Functional data depth. Oberwolfach Report No. 12/2016, pages 24-26.
  3. 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.
  4. 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.
  5. 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.
  6. 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