Position:
Lecturer
Department:
Department of Mathematics (DM)
Room:
NB 615
eMail:
Phone:
+421 259 325 296
Research activities:
Mathematical statistics, theory of estimation, predictions in regression models
Availability:

Publications

Book

  1. Š. Varga: Mathematical Statistics (in Slovak), Slovenská technická univerzita v Bratislave, 2012.

Chapter or pages in book

  1. Š. Varga: Modeling multidimensional data sets, In ZAMAT 2014, Proceedings of Applied Mathematics and Informatics, Editor(s): A. Kolesárová, M. Nehéz, pp. 17–25, 2014.
  2. Š. Varga: Robust estimations in fuzzy linear regression models. Quo Vadis Computational Intelligence, Editor(s): Physica-Verlag, Springer-Verlag Company, Physica-Verlag, Heidelberg, Germany, pp. 239–246, 2000.
  3. Š. Varga – M. Šabo: Linear regression with fuzzy variables. The State of the Art in Computational Intelligence, Editor(s): Physica-Verlag, Springer-Verlag Company, Physica-Verlag, Heidelberg, Germany, pp. 99–103, 2000.

Article in journal

  1. L. Dubinyová – M. Jablonský – Š. Varga – M. Fikar – S. Katuščák: Cellulose Materials Identification? The Effect of Dimensionality of Colour Photography Data. BioResources, no. 3, vol. 10, pp. 71–86, 2016.
  2. M. Jablonský – L. Dubinyová – Š. Varga – K. Vizárová – J. Šima – S. Katuščák: Cellulose Fibre Identification through Color Vectors of Stained Fibre. BioResources, no. 3, vol. 10, pp. 5845–5862, 2015.
  3. Š. Varga: Fuzzy predictions in regression models. Journal of Applied Mathematics, no. 2, vol. 4, pp. 245–252, 2010.
  4. Š. Varga: Another view on the fuzzy regression. Forum Statisticum Slovacum, no. 3, vol. 5, pp. 1–7, 2009.
  5. Š. Varga: Kernel Smoothing in Nonparametric Regression. Forum Statisticum Slovacum, no. 2, vol. IV, pp. 109–113, 2008.
  6. Š. Varga: Robust estimations in classical regression models versus robus estimations in fuzzy regression models. Kybernetika, no. 4, vol. 43, pp. 503–508, 2007.
  7. Š. Varga – M. Hladíková: Neural networks versus nonparametric regression. Forum Statisticum Slovacum, vol. 2, pp. 229–233, 2007.
  8. Š. Varga – I. Horváthová: Fuzzy regression models with asymmetric fuzzy numbers. Forum Statisticum Slovacum, vol. 2, pp. 234–239, 2007.
  9. Š. Varga: Regresné modely so zmiešanými efektmi (in Slovak). Forum Statisticum Slovacum, vol. 3, pp. 159–164, 2006.
  10. F. Malík – Š. Varga – D. Slugeň: Evaluation of wine tasters. Mitteilungen Klosterneuburg, no. 3-4, vol. 55, pp. 107–113, 2005.
  11. A. Szitás – G. Szeiffová – Š. Varga – J. Annus – M. Babiak: Recognition of wood species. Classification accuracy or RGB tristimulus values. Chemické Listy, vol. 99, pp. 618–619, 2005.
  12. Š. Varga: Classical regression models versus fuzzy regression models. Journal of Applied Mathematics, Statistics and Informatics, no. 2, vol. 1, pp. 95–101, 2005.
  13. Š. Varga – P. Grančič – S. Katuščák – A. Szitás: Predictions in neural networks and logistic regression (in Slovak). Forum Statisticum Slovacum, vol. 3, pp. 138–145, 2005.
  14. S. Katuščák – L. Kucera – Š. Varga: New method of recognition of wood species. Increasing of the effectivness of colorimetric recognition of picea excelsa and abies alba. Wood Res., no. 1, vol. 47, pp. 1–12, 2002.
  15. M. Šabo – A. Kolesárová – Š. Varga: RET operators generated by triangular norms and copulas. Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 9, pp. 169–181, 2001.
  16. M. Šabo – Š. Varga: A note on the relevancy transformation operator in fuzzy reasoning. Busefal, vol. 80, pp. 40–45, 1999.
  17. J. Mocák – P. Jurasek – G. Phillips – Š. Varga – E. Casadei: The classification of natural gums. Chemometric characterization of exudate gums.. pp. 141–150, 1998.
  18. Š. Varga: Estimations of Covariance Components in Mixed Linear Models. Mathematica Bohemica, no. 1, vol. 121, pp. 29–33, 1996.
  19. P. Jurasek – Š. Varga – G. Phillips: Classifications of natural gums. VII. Relationship between the series Vulgares. Food Hydrocolloids, no. 1, vol. 9, pp. 17–34, 1995.
  20. P. Jurasek – G. Phillips – Š. Varga: The classification of natural gums. VI. GumArabic derived from A- Senegal. Food Hydrocolloids, no. 6, vol. 8, pp. 567–588, 1994.
  21. Š. Varga: Quadratic Estimations in Mixed Linear Models. Applications of Mathematics, no. 2, vol. 36, pp. 134–144, 1991.
  22. Š. Varga: Minimum Variance Quadratic Unbiased Estimation of Variance Components. Mathematica Slovaca, no. 2, vol. 36, pp. 163–170, 1986.

Article in conference proceedings

  1. Š. Varga: Fuzzy predictions in regression models. Editor(s): M. Kováčová, In Aplimat 2010, STU, vol. 9, pp. 451–458, 2010.
  2. Š. Varga: Predictions in nonparametric regression. In Proceedings of International Seminar UNCERTAINTY 2008, pp. 111–116, 2008.
  3. Š. Varga: Regression models - Estimations. Editor(s): M. Fikar, A. Kolesárová, M. Bakošová, Slovak University of Technology Press, Bratislava, pp. 11–15, 2007.
  4. Š. Varga: Predictions in regres models with mixed effects (in Slovak). pp. CD, 6 strán, 2006.
  5. Š. Varga: Predictions in mixed effects models. In 5. Matematicky workshop 2006, Brno, pp. 123–124, 2006.
  6. M. Reháková – K. Vizárová – D. Jančiová – M. Valovičová – Š. Varga: Preselection of historical books in the process of their stabilization. pp. 47–48, 2004.
  7. Š. Varga: Classical regression models versus fuzzy regression models. pp. 109–115, 2004.
  8. Š. Varga: Non-parametric regression and smoothing. pp. 105–108, 2004.
  9. Š. Varga: On robust estimations in fuzzy regression models. pp. 415–419, 2002.
  10. Š. Varga: On arithmetic in fuzzy regression models. pp. 182–187, 2002.
  11. Š. Varga – F. Malík – J. Nemanič: Evaluation of wine tasters. pp. 307–313, 2002.
  12. Š. Varga: Uncertainty in regression models. pp. 154–159, 2001.
  13. Š. Varga: Different types of uncertainty in regression models. pp. 160–164, 2001.
  14. J. Mocák – Š. Varga: . (in Slovak). pp. 7–18, 2000.
  15. M. Šabo – Š. Varga: T – Norms in fuzzy regression models II. Dependence fuzzy regression on choice T – norm. pp. 115–117, 2000.
  16. Š. Varga – M. Šabo: T – Norms in fuzzy regression models I. Estimations in fuzzy regression models.. pp. 133–136, 2000.
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