doc. Mgr. David Kraus, Ph.D.

docent – Ústav matematiky a statistiky


kancelář: pav. 08/03012
Kotlářská 267/2
611 37 Brno

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telefon: 549 49 8473
e‑mail:
sociální a akademické sítě:
Životopis

Curriculum vitae

Department, faculty, university
  • Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno
Current position
  • Associate Professor of Statistics
Education and academic qualifications
  • 2008: Ph.D., Statistics, Charles University in Prague
  • 2003: M.Sc., Statistics, Charles University in Prague
Professional experience
  • Since 2022: Masaryk University Brno, Department of Mathematics and Statistics, Associate Professor of Statistics
  • 2016–2022: Masaryk University Brno, Department of Mathematics and Statistics, Assistant Professor of Statistics
  • 2013–2015: University of Bern, Institute of Social and Preventive Medicine, Statistician
  • 2012–2013: University Hospital Lausanne, Institute of Social and Preventive Medicine, Statistician
  • 2008–2012: Swiss Federal Institute of Technology in Lausanne (Ecole Polytechnique Fédérale de Lausanne, EPFL), Institute of Mathematics, Chair of Mathematical Statistics, Postdoctoral Researcher
  • 2005–2008: Institute of Information Theory and Automation, Prague, Research Assistant
  • 2004–2008: Charles University in Prague, Department of Statistics, Research Assistant, Teaching Assistant
Research
  • Most of my research work is connected with the broad topic of statistical inference with stochastic processes: data are represented as stochastic processes, models and methods of their analysis are motivated by, developed through or justified by theories and techniques for stochastic processes. In particular, my interests include functional data analysis, survival and event history analysis, point processes, inverse problems, non- and semi-parametric procedures, quantile methods, goodness-of-fit inference, robust analysis, applications in the natural sciences.
Major publications
  • KRAUS, David. Prediction intervals and bands with improved coverage for functional data under noisy discrete observation. Journal of Applied Statistics. Taylor and Francis Ltd., 2024. ISSN 0266-4763. Dostupné z: https://dx.doi.org/10.1080