Thesis supervisor: István Fazekas
Location of studies (in Hungarian): University of Debrecen Faculty of Informatics Abbreviation of location of studies: DE IK
Description of the research topic:
Syllabus
Obtaining new limit theorems in probability theory and statistics. Obtaining new inequalities, new strong and weak limit theorems. Considering statistical observations depending on space and/or time. Spatial and temporal statistical models. Linear and non-linear models. Parametric and nonparametric (kernel-type) estimations. Asymptotic results for the estimators. Numerical realization of the estimators and their study by computer simulation. Machine learning for statistics.
Bibliography
Bosq, D.: Nonparametric Statistics for Stochastic Processes. Springer, New York - Berlin – Heidelberg, 1998.
Tasos C. Christofides, István Fazekas, Milto Hadjikyriakou: Conditional acceptability of random variables, J. Ineq. Appl. pp. 1-18, 2016.
Cressie, N. A. C.:Statistics for Spatial Data. Wiley, New York, 1993.
DasGupta, Anirban: Asymptotic Theory of Statistics and Probability. Springer Texts in Statistics, 2008.
Fazekas I, Klesov O: A general approach to the strong law of large numbers, Th. Probab. Appl. 45: (3) pp. 436-449, 2000.
Fazekas, I.; Kukush, A. G.: Infill asymptotics inside increasing domains for the least squares estimator in linear models. Stat. Inf. Stoch. Proc. 3 (2000), 199-223.
A. W. van der Vaart: Asymptotic Statistics (Cambridge Series in Statistical and Probabilistic Mathematics) 2000.
Deadline for application: 2022-11-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).