Vladimir Panov
- Associate Professor:Faculty of Economic Sciences / Department of Statistics and Data Analysis
- Senior Research Fellow:Laboratory of Stochastic Analysis and its Applications
- Programme Academic Supervisor:Statistical Modelling and Actuarial Science
- Vladimir Panov has been at HSE University since 2013.
Education and Degrees
- 2012
PhD
Humboldt University of Berlin - 2008
Degree in Mathematics
Lomonosov Moscow State University
Membership in the professional societies
Benoulli society for mathematical statistics and probability
Courses (2020/2021)
- Nonparametric statistics (Master’s programme; Faculty of Economic Sciences; 1 year, 3 module)Eng
- Nonparametric Theory and Data Analysis (Master’s programme; International laboratory for Applied Network Research; 1 year, 3 module)Eng
- Nonparametric Theory and Data Analysis (Master’s programme; International laboratory for Applied Network Research; 2 year, 3 module)Eng
- Исследовательский проектный семинар (НИС) (Optional course (faculty); Faculty of Economic Sciences; 1-3 module)Rus
Stochastic Processes (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 4 year, 1, 2 module)Rus
Stochastic Processes (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 3 year, 1, 2 module)Rus
- Stochastic Processes (Bachelor’s programme; Faculty of Economic Sciences; 3 year, 1, 2 module)Eng
- Past Courses
Courses (2019/2020)
- Nonparametric statistics (Master’s programme; Faculty of Economic Sciences; 1 year, 3 module)Eng
- Nonparametric Theory and Data Analysis (Master’s programme; International laboratory for Applied Network Research; 2 year, 3 module)Eng
- Nonparametric Theory and Data Analysis (Master’s programme; International laboratory for Applied Network Research; 1 year, 3 module)Eng
Stochastic Processes (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 4 year, 1, 2 module)Rus
Stochastic Processes (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 3 year, 1, 2 module)Rus
Courses (2018/2019)
- High Dimensional Statistics (Master’s programme; Faculty of Economic Sciences; 1 year, 3 module)Eng
- Nonparametric Theory and Data Analysis (Master’s programme; International laboratory for Applied Network Research; 1 year, 3, 4 module)Eng
- Research Seminar (Master’s programme; Faculty of Economic Sciences; 1 year, 2-4 module)Rus
Stochastic Processes (Bachelor’s programme; Faculty of Economic Sciences; field of study "38.03.01. Экономика", field of study "38.03.01. Экономика", field of study "38.03.01. Экономика"; 3 year, 1, 2 module)Rus
Courses (2015/2016)
- Modelling Jump Stochastic Processes of Economic Dynamics (Master’s programme; Faculty of Economic Sciences; 2 year, 1, 2 module)Rus
Stochastic Processes (Bachelor’s programme; Faculty of Economic Sciences; "Совместная программа по экономике НИУ ВШЭ и РЭШ"; field of study "38.03.01. Экономика"; 4 year, 1, 2 module)Rus
Stochastic Processes (Bachelor’s programme; Faculty of Economic Sciences; "Экономика и статистика", "Совместная программа по экономике НИУ ВШЭ и РЭШ"; field of study "38.03.01. Экономика"; 3 year, 1, 2 module)Rus
Student Term / Thesis Papers
- Bachelor
A. Zhilyaev, Modelling of Sharing Systems via Renewal Processes. Faculty of Economic Sciences, 2019
M. Lunitsin, Stochastic Models Based on the Time-Change Technique. HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE), 2019
I. Bouyukliiski, Estimation of Value-at-risk by Extreme Value Methods. Faculty of Economic Sciences, 2019
A. Kadyrbekov, Modeling Dependence between Financial Characteristics via Multivariate Stable Processes. Faculty of Economic Sciences, 2019
A. Shuvalov, Option Pricing Using Levy Processes. Faculty of Economic Sciences, 2018
B. Tagarova, Methods of Statistical Dimention Reduction. School of Statistics, Data Analysis and Demography, 2014
- Master
B. Iakashev, Stochastic Volatility Models with Jumps. Faculty of Economic Sciences, 2018
A. Kobyakova, Parameter Estimation in Gaussian Observations Model with Delay Having Singularity. Faculty of Economic Sciences, 2018
T. Tregubova, Non-Gaussian Models of Dependence in Asset Returns. Faculty of Economic Sciences, 2018
E. Samarin, Modelling of Multivariate Subordinated Processes. Faculty of Economic Sciences, 2018
N. Vaganov, Modelling of Multivariate Time-changed Stable Processes. Faculty of Computer Science, 2017
O. Iaksin, Lévy-Based Stochastic Volatility Models. Faculty of Economic Sciences, 2016
Books2
- Book Modern problems of stochastic analysis and statistics - Selected contributions in honor of Valentin Konakov / Ed. by V. Panov. Heidelberg : Springer, 2017. doi
- Book Härdle W., Spokoiny V., Panov V., Wang W. Basics of modern mathematical statistics: exercises and solutions Issue XXV. Heidelberg : Springer, 2013.
Articles15
- Article Konakov V., Panov V., Piterbarg V. Extremes of a class of non-stationary Gaussian processes and maximal deviation of projection density estimates // Extremes. 2021 doi
- Article Novikova O., Nosov V., Panov V., Novikova E., Krasnopolskaya K., Andreeva Y., Shevchuk A. Live births and maintenance with levonorgestrel IUD improve disease-free survival after fertility-sparing treatment of atypical hyperplasia and early endometrial cancer // Gynecologic Oncology. 2021. Vol. 161. No. 1. P. 152-159. doi
- Article Molchanov S., Panov V. The Dickman–Goncharov distribution // Russian Mathematical Surveys. 2020. Vol. 75. No. 6. P. 1089-1132. doi
- Article Molchanov S., Panov V. Limit theorems for the alloy-type random energy model // Stochastics-An International Journal of Probability and Stochastic Processes. 2019. Vol. 91. No. 5. P. 754-772. doi
- Article Belomestny D., Panov V., Woerner J. Low-frequency estimation of continuous-time moving average Levy processes // Bernoulli: a journal of mathematical statistics and probability. 2019. Vol. 25. No. 2. P. 902-931. doi
- Article Panov V., Samarin E. Multivariate asset-pricing model based on subordinated stable processes // Applied Stochastic Models in Business and Industry. 2019. Vol. 35. P. 1060-1076. doi
- Article Panov V. Some properties of the one-dimensional subordinated stable model // Statistics and Probability Letters. 2019. Vol. 146. P. 80-84. doi
- Article Belomestny D., Orlova T., Panov V. Statistical inference for moving-average Lévy-driven processes: Fourier-based approach // Statistica Neerlandica. 2019. Vol. 1. P. 100-117. doi
- Article Belomestny D., Panov V. Semiparametric estimation in the normal variance-mean mixture model // Statistics. 2018. Vol. 52. No. 3. P. 571-589. doi
- Article Panov V. Limit theorems for sums of random variables with mixture distribution // Statistics and Probability Letters. 2017. Vol. 129. P. 379-386. doi
- Article Panov V. Series representations for multivariate time-changed Levy models // Methodology and Computing in Applied Probability. 2017. Vol. 19. No. 1. P. 97-119. doi
- Article Konakov V., Panov V. Sup-norm convergence rates for Levy density estimation // Extremes. 2016. Vol. 19. No. 3. P. 371-403. doi
- Article Belomestny D., Panov V. Statistical inference for generalized Ornstein-Uhlenbeck processes // Electronic journal of statistics. 2015. Vol. 9. No. 2. P. 1974-2006. doi
- Article Belomestny D., Panov V. Abelian theorems for stochastic volatility models with application to the estimation of jump activity // Stochastic Processes and their Applications. 2013. Vol. 123. No. 1. P. 15-44.
- Article Belomestny D., Panov V. Estimation of the activity of jumps in time-changed Levy models // Electronic journal of statistics. 2013. Vol. 7. P. 2970-3003.
Other publications4
- Preprint Panov V., Морозова Е. А. Extreme value analysis for mixture models with heavy-tailed impurity / Cornell University. Series arxive "math". 2021. No. 2103.07689.
- Article Belomestny D., Panov V., Spokoiny V. Semiparametric estimation of the signal subspace // Journal of machine learning and data analysis. 2012. Vol. 1. No. 3. P. 140-147.
- Preprint Panov V. Estimation of the signal subspace without estimation of the inverse covariance matrix / Humboldt-Universität zu Berlin. Series Discussion paper SFB 649 "Economic risk". 2010. No. 2010-050.
- Preprint Panov V. Non-Gaussian Component Analysis: New Ideas, New Proofs, New Applications / Humboldt-Universität zu Berlin . Series Discussion paper SFB 649 "Economic risk". 2010. No. 2010-026.
Selected talks at international conferences
- 11th Extreme value analysis conference - EVA2019 (Zagreb, July 2019)
- Stochastic models, statistics and their application- SMSA2019 (Dresden, March 2019)
- International workshop on applied probability - IWAP2018 (Budapest, June 2018)
- 13th German probability and statistics days - GPSD2018 (Freiburg, February 2018)
- Conference on ambit fields and related topics (Aarhus, August 2017)
- 10th Extreme value analysis conference – EVA2017 (Delft, June 2017)
- Probability seminar Essen (Essen, June 2017)
- Modern econometric tools and applications – META2017 (Nizhnij Novgorod, June 2017)
- World congress in probability and statistics (Toronto, July 2016)
- Fractality and fractionality (Leiden, May 2016)
- 12th German probability and statistics days (Bochum, March 2016)
- 38th conference on stochastic processes and their applications - SPA (Oxford, July 2015)
- European meeting of statisticians - EMS (Amsterdam, July 2015)
- Probability seminar Essen (Essen, June 2015)
- Statistical inference for Levy processes (Leiden, September 2014)
- Probability seminar Essen (Essen, June 2014)
- Advanced finance and stochastics (Moscow, June 2013)
- Advances in predictive modeling and optimization (Berlin, May 2013)
Talks at the workshops organized by LSA:
- LSA Autumn meeting - 2020 (Zoom)
- LSA Winter meeting - 2019 (Snegiri, Moscow Region, December 2019)
- LSA Winter meeting - 2018 (Snegiri, Moscow Region, December 2018)
- LSA Summer meeting - 2018 (Moscow, June 2018)
- LSA Winter meeting - 2017 (Snegiri, Moscow Region, December 2017)
- Statistics meets stochastics - 2 (Moscow, June 2017)
- New perspectives in stochastic analysis (Snegiri, Moscow Region, December 2016)
- New trends in stochastic analysis (Snegiri, Moscow Region, December 2015)
- Non-parametric and high-dimensional statistics (Heidelberg, July 2015)
- Frontiers of high-dimensional statitistics, optimization and econometrics (Moscow, February 2015)
- Statistics meets stochastics (Snegiri, Moscow Region, November 2014)
- Advances in stochastic analysis (Moscow, September 2014)
Studying Applied Statistics and Network Analysis at HSE Moscow
Felipe Vaca Ramirez and Paco Arevalo Reyes, both from Ecuador, are second-year students in HSE’s Master’s programme in ‘Applied Statistics with Network Analysis’. Having heard about Russia’s rich mathematical tradition and the high academic standing of HSE, they both decided to study in Moscow despite how far away from home it is. Felipe and Paco share a background in economics, and the HSE programme’s focus on statistics and data and network analysis was a huge draw for them. Affordable tuition fees and multicultural environment were additional bonuses.
Coursera Offers New Courses by HSE Lecturers
From late March and early April, HSE will offer four new coursers on Coursera on intercultural communication, machine learning, computer vision, and stochastic processes.
