Vladimir Spokoiny

Professor

v.spokoiny@skoltech.ru

Research Interests

  • Image analysis and its applications to medicine
  • Adaptive nonparametric smoothing and hypothesis testing
  • High dimensional data analysis
  • Statistical methods in finance
  • Nonlinear nonstationary time series

 

Students

Teaching

Selected publications

2016:
  • A. Kalinina, A. Suvorikova, V. Spokoiny, M. Gelfand, Detection of homologous recombination in closely related strains, Journal of Bioinformatics and Computational Biology, 14 (2016) pp. 1641001/1–1641001/12.
  • A. Andresen, V. Spokoiny. Convergence for an alternation maximization procedure. Journal of Machine Learning Research, 17, pp. 1-53, arXiv:1501.01525.
  • V. Spokoiny, N. Willrich. Bootstrap tuning in ordered model selection. arXiv:1507.05034.
2015:
  • Y. Chen, V. Spokoiny. Modeling nonstationary and leptokurtic financial time series. Econometric Theory, pp. 703-728.
  • A. Gasnikov, Y. Nesterov, V. Spokoiny. On the efficiency of a randomized mirror descent algorithm in online optimization problems. Computational Mathematics and Mathematical Physics, 55, pp. 580-596.
  • A. Gasnikov, P. Dvurechensky, D. Kamzolov, Y. Nesterov, V. Spokoiny, P. Stetsyuk, A. Suvorikova, A. Chernov, Searching for equilibriums in multistage transport models (in Russian). Proceedings of Moscow Institute of Physics and Technology, 7, pp. 143-155.
  • V.G. Gitis, A.B. Derendyaev, S.A. Pirogov, V. Spokoiny, E.F. Yurkov. Adaptive estimation of seismic parameter fields from earthquakes catalogs. Journal of Communications Technology and Electronics, 60, pp. 1459-1465.
  • M. Panov, V. Spokoiny, Finite sample Bernstein – von Mises theorem for semiparametric problems. Bayesian Analysis, 10, pp. 665-710, arXiv:1310.7796.
  • P. Dvurechensky, Y. Nesterov, V. Spokoiny. Primal-dual methods for solving infinite-dimensional games. Journal of Optimization Theory and Applications, 166, pp. 23-51.
  • V. Spokoiny, M. Zhilova. Bootstrap confidence sets under a model misspecification. The Annals of Statistics, 43, pp. 2653-2675, arXiv:1410.0347.
2014:
  • A. Andresen, V. Spokoiny. Critical dimension in profile semiparametric estimation. Electronic Journal of Statistics, 8, pp. 3077-3125, arXiv:1303.4640.
  • N. Baldin, V. Spokoiny. Bayesian model selection and the concentration of the posterior of hyperparameters. Journal of Mathematical Sciences, 203, pp. 761-776.
  • D. Belomestny, V. Spokoiny. Concentration inequalities for smooth random fields. Theory of Probability and its Applications, 58, pp. 314-323, arXiv:1307.1565.
  • G. Milshteyn, V. Spokoiny. Construction of mean-self-financing strategies for European options under regime-switching. SIAM Journal on Financial Mathematics, 5, pp. 532-556.
  • M. Panov, V. Spokoiny. Critical Dimension in Semiparametric Bernstein – von Mises Theorem. Proceedings of Steklov Mathematical Institute, 287, pp. 242–266.
  • A. Zaytsev, E. Burnaev, V. Spokoiny. Properties of the Bayesian parameter estimation of a regression based on Gaussian processes. Journal of Mathematical Sciences, 203, pp. 789-798.
2013:
  • M. Zhilova, V. Spokoiny. Uniform properties of the local maximum likelihood estimate. Automation and Remote Control, 74, pp. 1656-1669.
  • E. Burnaev, A. Zaytsev, V. Spokoiny. Non-asymptotic properties for Gaussian field regression. Automation and Remote Control, 74, pp. 1645-1655.
  • E. Burnaev, A. Zaitsev, V. Spokoiny. Bernstein – von Mises theorem for regression based on Gaussian processes. Russian Mathematical Surveys, 68, pp. 954-956.
  • E. Diederichs, A. Juditsky, A. Nemirovski, V. Spokoiny. Sparse non Gaussian component analysis by semidefinite programming. Journal of Machine Learning Research, pp. 1-28.
  • F. Gach, R. Nickl, V. Spokoiny. Spatially adaptive density estimation by localised Haar projections. Annales de l’Institut Henri Poincare, Probabilites et Statistiques, 49, pp. 900-914, arXiv:1111.2807.
  • A. Zaitsev, E. Burnaev, V. Spokoiny. Properties of the posterior distribution of a regression model based on Gaussian random fields. Automation and Remote Control, 74, pp. 1645-1655.
  • V. Spokoiny, M. Zhilova. Sharp deviation bounds for quadratic forms. Mathematical Methods of Statistics, 22, pp. 100-113, arXiv:1302.1699.
  • V. Spokoiny, W. Wang, W. Härdle. Local quantile regression (with rejoinder). Journal of Statistical Planning and Inference, 143, pp. 1109-1129, arXiv:1208.5384.
  • E. Diederichs, A. Juditsky, A. Nemirovski, V. Spokoiny. Sparse non Gaussian component analysis by semidefinite programming. Journal of Machine Learning Research, pp. 1-28, arXiv:1106.0321.
2012:
  • V. Spokoiny. Parametric estimation. Finite sample theory. The Annals of Statistics, 40, pp. 2877-2909, arXiv:1111.3029.
2010:
  • Y. Chen, W. Härdle, V. Spokoiny. GHICA — Risk analysis with GH distributions and independent components. Journal of Empirical Finance, 17, pp. 255-269.

Further information

Skoltech profile
Google Scholar
Researchgate