World's Best Scientists 2026 revealed!
Akimichi Takemura

Akimichi Takemura

D-Index & Metrics

Mathematics

D-Index
31
Citations
3572
World Ranking
3365
National Ranking
55

Akimichi Takemura 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 Akimichi Takemura 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: 83 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: 322 publications — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Akimichi Takemura 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 Akimichi Takemura 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: 138 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: 31 D-Index — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Overview

Akimichi Takemura is affiliated with Shiga University in Japan and has contributed to research primarily within the fields of Mathematics and Environmental Science. Their scholarly work extends across subfields including Statistics and Probability, Environmental Engineering, Applied Mathematics, Artificial Intelligence, and Computational Mechanics.

Their main research topics cover a range of themes such as Soil Geostatistics and Mapping, Statistical Methods and Inference, Point Processes and Geometric Inequalities, Remote Sensing and LiDAR Applications, Bayesian Methods and Mixture Models, Advanced Numerical Analysis Techniques, and Machine Learning and Algorithms.

Takemura has published work in several venues, with frequent publications appearing in:

  • arXiv (Cornell University)
  • Journal of Multivariate Analysis
  • Japanese Journal of Statistics and Data Science

Collaborations have included partnerships with authors such as:

  • Yuzo Maruyama
  • Satoshi Kuriki
  • Jonathan Taylor

Recent papers authored or co-authored by Takemura include:

  • The volume-of-tube method for Gaussian random fields with inhomogeneous variance, 2021, Journal of Multivariate Analysis
  • The volume-of-tube method for Gaussian random fields with inhomogeneous variance, 2021, arXiv (Cornell University)
  • Non-minimaxity of debiased shrinkage estimators, 2023, arXiv (Cornell University)
  • Non-minimaxity of debiased shrinkage estimators, 2023, Japanese Journal of Statistics and Data Science
  • A new perspective on dominating the James-Stein estimator, 2025, arXiv (Cornell University)

Best Publications

  • An Asymptotically Optimal Bandit Algorithm for Bounded Support Models.

    Junya Honda;Akimichi Takemura

  • Markov Bases in Algebraic Statistics

    Satoshi Aoki;Hisayuki Hara;Akimichi Takemura

  • Validity of the expected Euler characteristic heuristic

    Jonathan Taylor;Akimichi Takemura;Robert J. Adler

  • Minimal Basis for a Connected Markov Chain over 3 x 3 x K Contingency Tables with Fixed Two-Dimensional Marginals

    Satoshi Aoki;Akimichi Takemura

  • Some characterizations of minimal Markov basis for sampling from discrete conditional distributions

    Akimichi Takemura;Satoshi Aoki

  • An orthogonally invariant minimax estimator of the covariance matrix of a multivariate normal population

    Akimichi Takemura

  • Tensor Analysis of ANOVA Decomposition

    Akimichi Takemura

  • On connectivity of fibers with positive marginals in multiple logistic regression

    Hisayuki Hara;Akimichi Takemura;Ruriko Yoshida

  • Holonomic gradient descent and its application to the Fisher-Bingham integral

    Hiromasa Nakayama;Kenta Nishiyama;Masayuki Noro;Katsuyoshi Ohara

  • WHY DO NONINVERTIBLE ESTIMATED MOVING AVERAGES OCCUR

    T. W. Anderson;Akimichi Takemura

  • Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors

    Junya Honda;Akimichi Takemura

  • ON THE EQUIVALENCE OF THE TUBE AND EULER CHARACTERISTIC METHODS FOR THE DISTRIBUTION OF THE MAXIMUM OF GAUSSIAN FIELDS OVER PIECEWISE SMOOTH DOMAINS

    Akimichi Takemura;Satoshi Kuriki

  • The holonomic gradient method for the distribution function of the largest root of a Wishart matrix

    Hiroki Hashiguchi;Yasuhide Numata;Nobuki Takayama;Akimichi Takemura

  • Defensive forecasting

    Vladimir Vovk;Akimichi Takemura;Glenn Shafer

  • Tail probabilities of the maxima of multilinear forms and their applications

    Satoshi Kuriki;Akimichi Takemura

  • Markov chain Monte Carlo exact tests for incomplete two-way contingency tables

    Satoshi Aoki;Akimichi Takemura

  • An asymptotically optimal policy for finite support models in the multiarmed bandit problem

    Junya Honda;Akimichi Takemura

  • Empirical characteristic function approach to goodness-of-fit tests for the Cauchy distribution with parameters estimated by MLE or EISE

    Muneya Matsui;Akimichi Takemura

  • Goodness-of-fit tests for symmetric stable distributions—Empirical characteristic function approach

    Muneya Matsui;Akimichi Takemura

  • Inadmissability of non-order-preserving orthogonally invariant estimators of the covariance matrix in the case of Stein's loss

    Yo Sheena;Akimichi Takemura

  • Non-asymptotic analysis of a new bandit algorithm for semi-bounded rewards

    Junya Honda;Akimichi Takemura

  • Weights of $overline{\chi}{}\sp 2$ distribution for smooth or piecewise smooth cone alternatives

    Akimichi Takemura;Satoshi Kuriki

  • Holonomic Gradient Descent and its Application to Fisher-Bingham Integral

    Tomonari Sei;Nobuki Takayama;Akimichi Takemura;Hiromasa Nakayama

  • Holonomic gradient method for the distribution function of the largest root of a Wishart matrix

    Hiroki Hashiguchi;Yasuhide Numata;Nobuki Takayama;Akimichi Takemura

Frequent Co-Authors

Glenn Shafer
Glenn Shafer Rutgers, The State University of New Jersey
Hyundong Shin
Hyundong Shin Kyung Hee University
Takayuki Hibi
Takayuki Hibi Osaka University
Jonathan Taylor
Jonathan Taylor Stanford University
Taiji Suzuki
Taiji Suzuki University of Tokyo
T. W. Anderson
T. W. Anderson Stanford University
Robert J. Adler
Robert J. Adler Technion – Israel Institute of Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Mathematics in the USA, exploring complementary online degrees can broaden career opportunities. Many graduates consider business-oriented paths such as an MBA to gain leadership and management skills. Programs listed under easiest mba specialization offer accessible options for those looking to enhance their qualifications without intensive admission barriers.

Additionally, if affordability is a key concern, prospective students may want to examine the most affordable online dba programs. These doctoral programs combine advanced business acumen with analytical rigor, making them suitable for mathematicians targeting executive roles.

Finance is another prominent field that values strong quantitative skills. Online master’s degrees in finance have become increasingly accessible, with numerous institutions offering flexible learning options. Interested candidates should review the options available under online masters in finance programs to find budget-friendly opportunities.

For those eager to balance ease of access and credibility, the easiest mba programs provide pathways that combine convenience with valuable credentials to complement a strong mathematics background.

Best Scientists Citing Akimichi Takemura

Trending Scientists

Recently Published Articles