University

National research Tomsk state university

Level of English language proficiency

Intermediate

The field of study for which the graduate student will be
accepted

01.06.01 - Mathematics and Mechanics,
09.06.01 - Computer Science and Computer engineering

The code of the field of study for which the graduate student
will be accepted

01.06.01,

09.06.01

List of research projects of a potential supervisor
(participation / leadership)

1. Econometric and probabilistic methods for the analysis of complex financial markets, grant RGNF, Head

2. Effective statistical methods of synthesis and analysis of improved robust signal processing algorithms, grant from the President, Head

3. Effective statistical methods for the analysis of epidemiological models, RGNF grant, contractor

4. New robust effective statistical methods of signal and image processing in stochastic systems, grant RNF, performer

5. Effective and robust methods of identification of dynamic stochastic systems under conditions of various a priori uncertainty, grant of the D.I. Mendeleev TSU Scientific Foundation, performer

List of possible research topics

1. Efficient Estimation in Regression Models with Small Noise

2. Adaptive methods for identifying of the stochastic dynamical systems described by diffusion processes


3. Improved model selection methods for signals and images estimation observed under semi-Markov noises

4. Improved model selection methods for big data mining

Supervisor’s
main publication

Pchelintsev E.A., Pergamenshchikov S.M. Oracle inequalities for the stochastic differential equations. Statistical Inference for Stochastic Processes. 2018. Vol. 21, № 2. P. 469-483.

Pchelintsev E., Pchelintsev V. and Pergamenshchikov S.M. Improved robust model selection methods for a Lévy nonparametric regression in continuous time. Journal of Nonparametric Statistics. 2019. Vol. 31, No 3. P. 612-628.

Pchelintsev E., Pergamenshchikov S., Povzun M. Efficient estimation methods for non-Gaussian regression models in continuous time. Annals of the Institute of Statistical Mathematics. 2021. https://doi.org/10.1007/s10463-021-00790-7

Pchelintsev E., Pergamenshchikov S., Leshchinskaya M. Improved estimation method for high dimension semimartingale regression models based on discrete data. Statistical Inference for Stochastic Processes. 2021. https://doi.org/10.1007/s11203-021-09258-0

Pchelintsev E., Pergamenshchikov S. Efficient Improved Estimation Method for Non-Gaussian Regression from Discrete Data // Springer Proceedings in Mathematics and Statistics (In book: Recent Developments in Stochastic Methods and Applications). 2021, 371, P. 164–177. https://doi.org/ 10.1007/978-3-030-83266-7_12
Lisovskaya E.Yu., Moiseev A.N., Moiseeva S.P., Pagano M.Modeling of Mathematical Processing of Physics Experimental Data in the Form of a Non-Markovian Multi-Resource Queuing System //Russian Physics Journal. 2019 Vol. 61, № 12 P. 2188-2196
Antonova I.S., Pchelintsev E.A. Econometric Modeling of Creative Industries Concentration Process in the Siberian and the Urals Single-Industry Towns // Mathematics. 2023. Vol. 11, № 17. Art. num. 3704. URL: https://www.mdpi.com/2227-7390/11/17/3704.
Murzintseva A., Pergamenchtchikov S., Pchelintsev E. A. Hedging Problem for Asian Call Options with Transaction Costs // Theory of Probability and Its Applications. 2023. Vol. 68, № 2. P. 211‒230. DOI: 10.1137/S0040585X97T991374
Pchelintsev E.A., Pergamenshchikov S.M. Sequential change-point detection for Markov chains // Теория вероятностей и её применения. 2023. Vol. 68, № 4.
Simulation of a Neural Network Model Identification Algorithm / Pchelintsev E., Perelevskiy S.S., Terekhov A., Korableva L. [et al] // Lecture Notes in Networks and Systems. 2023. Vol. 722. P. 229‒236. DOI: 10.1007/978-3-031-35311-6_25
Pchelintsev E.A., Perelevskiy S.S. On estimation for the trend coefficient of a diffusion process by discrete time observations // Theory of Probability and Its Applications. 2023. Vol. 68, № 1. P. 181‒182. DOI: 10.4213/tvp5608
Pchelintsev E.A., Pergamenshchikov S.M., Tenzin R.O. Non-asymptotic sequential change-point detection for Markov chains with applications in the epidemic statistical analysis // Sequential Analysis. 2023. Vol. In print. P. 1‒14.
Antonova I.S., Pchelintsev E.A. Path dependence and regional disparities in single-industry towns in Russia: the evidence from micro data // International Journal of Economic Policy in Emerging Economies. 2022. Vol. 16, № 2-4. P. 318‒343. DOI: 10.1504/IJEPEE.2022.126623
Pchelintsev E., Pergamenshchikov S. M., Leshchinskaya M. Efficient estimation methods for non-Gaussian regression models in continuous time //Annals of the Institute of Statistical Mathematics. 2022. Vol. 74, № 1. P. 113-142.
Pchelintsev E.A., Pergamenshchikov S. M., Leshchinskaya M. Improved estimation method for high dimension semimartingale regression models based on discrete data // Statistical Inference for Stochastic Processes. 2022. Vol. 25, № 1. P. 537‒576. DOI: 10.1007/s11203-021-09258-0

Supervisor’s specific requirements

- Model selection methods for nonparametric estimation of signals and images determined by linear stochastic differential equations with non-Gaussian disturbances.
- Improvement of non-asymptotic quality of statistical estimation in mean square accuracy.
- Development of efficient estimation methods for general semimartingale regression models in continuous time
- Development of statistical machine learning methods for big data processing

1. Advanced knowledge of methods in statistics of stochastic processes.
2. Ability to program and process data in Python / R

Supervisor’s research interests