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D-Index & Metrics

Mathematics

D-Index
43
Citations
7807
World Ranking
1694
National Ranking
99

Vladimir Spokoiny publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Vladimir Spokoiny sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 82 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 202 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Vladimir Spokoiny D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Vladimir Spokoiny sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 137 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 43 D-Index — 54th percentile

54% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Vladimir Spokoiny is affiliated with the Weierstrass Institute for Applied Analysis and Stochastics in Germany. Their research spans key domains in mathematics and computer science, with a focus on statistical and computational methods.

The main fields of study in Vladimir Spokoiny's work include:

  • Mathematics
  • Computer Science

Their research explores various subfields such as:

  • Statistics and Probability
  • Artificial Intelligence
  • Computational Mechanics
  • Computational Theory and Mathematics
  • Numerical Analysis

Main topics covered in Vladimir Spokoiny's publications are:

  • Statistical Methods and Inference
  • Sparse and Compressive Sensing Techniques
  • Markov Chains and Monte Carlo Methods
  • Statistical Methods and Bayesian Inference
  • Stochastic Gradient Optimization Techniques
  • Optimization and Variational Analysis
  • Gaussian Processes and Bayesian Inference

Vladimir Spokoiny has published extensively, contributing to journals and proceedings frequently. Prominent venues for their work include:

  • arXiv (Cornell University)
  • Bernoulli
  • Communications in Mathematical Sciences
  • The Annals of Applied Probability
  • IFAC-PapersOnLine

Recent papers authored or co-authored by Vladimir Spokoiny include:

  • Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation, 2023, SIAM/ASA Journal on Uncertainty Quantification
  • Sharper dimension-free bounds on the Frobenius distance between sample covariance and its expectation, 2025, Bernoulli
  • Accelerated gradient methods with absolute and relative noise in the gradient, 2023, Optimization Methods & Software
  • On the line-search gradient methods for stochastic optimization, 2020, IFAC-PapersOnLine
  • Statistical inference for Bures-Wasserstein barycenters, 2021, The Annals of Applied Probability

Frequent collaboration is a characteristic of Vladimir Spokoiny's work. Common coauthors include:

  • Nikita Puchkin
  • Alexandra Suvorikova
  • Alexander Gasnikov
  • Pavel Dvurechensky
  • Denis Belomestny

Best Publications

  • Random Gradient-Free Minimization of Convex Functions

    Yurii Nesterov;Vladimir Spokoiny

  • An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model Against a Nonparametric Alternative

    Joel L. Horowitz;Vladimir G. Spokoiny

  • Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors

    O. V. Lepski;E. Mammen;V. G. Spokoiny

  • Direct estimation of the index coefficient in a single-index model

    Marian Hristache;Anatoli Juditsky;Vladimir Spokoiny

  • Adaptive weights smoothing with applications to image restoration

    J. Polzehl;V. G. Spokoiny

  • Adaptive hypothesis testing using wavelets

    V. G. Spokoiny

  • Optimal pointwise adaptive methods in nonparametric estimation

    O. V. Lepski;V. G. Spokoiny

  • Propagation-Separation Approach for Local Likelihood Estimation

    Jörg Polzehl;Vladimir Spokoiny

  • Multiscale testing of qualitative hypotheses

    Lutz Dümbgen;Vladimir G. Spokoiny

  • Structure Adaptive Approach for Dimension Reduction

    Marian Hristache;Anatoli Juditsky;Jörg Polzehl;Vladimir Spokoiny

  • Statistical inference for time-inhomogeneous volatility models

    Danilo Mercurio;Vladimir G. Spokoiny

  • Parametric estimation. Finite sample theory

    Vladimir Spokoiny

  • Multiscale local change point detection with applications to value-at-risk

    Vladimir Spokoiny

  • ESTIMATION OF A FUNCTION WITH DISCONTINUITIES VIA LOCAL POLYNOMIAL FIT WITH AN ADAPTIVE WINDOW CHOICE

    Vladimir G. Spokoiny

  • Inhomogeneous Dependence Modeling with Time-Varying Copulae

    Enzo Giacomini;Wolfgang Härdle;Vladimir Spokoiny

  • Minimax nonparametric hypothesis testing: the case of an inhomogeneous alternative

    Oleg V. Lepski;Vladimir G. Spokoiny

  • On estimation of the Lr norm of a regression function

    O. Lepski;A. Nemirovski;V. Spokoiny

  • Analyzing fMRI experiments with structural adaptive smoothing procedures

    Karsten Tabelow;Jörg Polzehl;Henning U. Voss;Vladimir G. Spokoiny

  • In Search of Non-Gaussian Components of a High-Dimensional Distribution

    Gilles Blanchard;Gilles Blanchard;Motoaki Kawanabe;Masashi Sugiyama;Masashi Sugiyama;Vladimir Spokoiny

  • Testing a Statistical Hypothesis

    Vladimir Spokoiny;Thorsten Dickhaus

Frequent Co-Authors

Wolfgang Karl Härdle
Wolfgang Karl Härdle Humboldt-Universität zu Berlin
Anatoli Juditsky
Anatoli Juditsky Grenoble Alpes University
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Motoaki Kawanabe
Motoaki Kawanabe Advanced Telecommunications Research Institute International
Yurii Nesterov
Yurii Nesterov Université Catholique de Louvain
Arkadi Nemirovski
Arkadi Nemirovski Georgia Institute of Technology
Albert N. Shiryaev
Albert N. Shiryaev Steklov Mathematical Institute
Friedrich Götze
Friedrich Götze Bielefeld University
Enno Mammen
Enno Mammen Heidelberg University

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