World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
37
Citations
5702
World Ranking
2504
National Ranking
1044

Liang Peng 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 Liang Peng 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: 194 publications — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Liang Peng 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 Liang Peng 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: 37 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2012 - Fellow of the American Statistical Association (ASA)

Overview

Liang Peng is affiliated with Georgia State University in the United States. Their academic work is primarily situated within the fields of Economics, Econometrics, and Finance, with a total of 55 publications contributing to this broad area.

Their research spans several subfields including Finance, Statistics and Probability, Economics and Econometrics, Management Science and Operations Research, and General Economics, Econometrics and Finance.

Key topics in Liang Peng's work include:

  • Financial Risk and Volatility Modeling
  • Monetary Policy and Economic Impact
  • Statistical Methods and Inference
  • Complex Systems and Time Series Analysis
  • Financial Markets and Investment Strategies
  • Forecasting Techniques and Applications
  • Advanced Statistical Methods and Models

Liang Peng has published frequently in several academic venues, notably:

  • SSRN Electronic Journal (14 publications)
  • Journal of Business and Economic Statistics (3 publications)
  • Journal of Econometrics (3 publications)
  • Insurance Mathematics and Economics (3 publications)
  • arXiv (Cornell University) (3 publications)

Some of the recent papers authored or co-authored include:

  • "Fault-tolerant interval inversion for accelerated bridge construction based on geometric nonlinear redundancy of cable system," 2021, Automation in Construction
  • "Risk Analysis via Generalized Pareto Distributions," 2021, Journal of Business and Economic Statistics
  • "Inference for conditional value-at-risk of a predictive regression," 2020, The Annals of Statistics
  • "Efficiently Backtesting Conditional Value-at-Risk and Conditional Expected Shortfall," 2020, Journal of the American Statistical Association
  • "Two-step risk analysis in insurance ratemaking," 2020, Scandinavian Actuarial Journal

Frequent co-authors collaborating with Liang Peng include:

  • Bingduo Yang
  • Xiaohui Liu
  • Lei Jiang
  • Yi He
  • Wei Long

In 2012, Liang Peng was recognized as a Fellow of the American Statistical Association (ASA).

Best Publications

  • Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation

    J. Danielsson;L. de Haan;L. Peng;C.G. de Vries

  • Comparison of tail index estimators

    L. De Haan;L. Peng

  • Least absolute deviations estimation for ARCH and GARCH models

    Liang Peng;Qiwei Yao

  • Asymptotically unbiased estimators for the extreme-value index

    L. Peng

  • On optimising the estimation of high quantiles of a probability distribution

    A. Ferreira;L. de Haan;L. Peng

  • A Bootstrap-based Method to Achieve Optimality in Estimating the Extreme-value Index

    G. Draisma;L. de Haan;L. Peng;T.T. Pereira

  • Bounds for the sum of dependent risks and worst Value-at-Risk with monotone marginal densities

    Ruodu Wang;Ruodu Wang;Liang Peng;Jingping Yang

  • Interval estimation of value-at-risk based on GARCH models with heavy-tailed innovations

    Ngai Hang Chan;Shi-Jie Deng;Liang Peng;Zhendong Xia

  • Estimation of the coefficient of tail dependence in bivariate extremes

    L. Peng

  • Almost sure convergence in extreme value theory

    Shihong Cheng;Liang Peng;Yongcheng Qi

  • Optimality Condition for Selected Mapping in OFDM

    G.T. Zhou;Liang Peng

  • Effects of data dimension on empirical likelihood

    Song Xi Chen;Liang Peng;Ying-Li Qin

  • Robust Estimation of the Generalized Pareto Distribution

    Liang Peng;Liang Peng;A.H. Welsh;A.H. Welsh

  • Estimating the mean of a heavy tailed distribution

    Liang Peng

  • Empirical likelihood confidence regions for comparison distributions and roc curves

    Gerda Claeskens;Bing-Yi Jing;Liang Peng;Wang Zhou

  • Confidence intervals for the tail index

    Shihong Cheng;Liang Peng

  • Semi-parametric Estimation of the Second Order Parameter in Statistics of Extremes

    M. Ivette Gomes;Laurens de Haan;Liang Peng

  • Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case

    Claudia Klüppelberg;Gabriel Kuhn;Liang Peng

  • Smoothed jackknife empirical likelihood method for ROC curve

    Yun Gong;Liang Peng;Yongcheng Qi

  • Estimating the tail dependence function of an elliptical distribution

    Claudia Klüppelberg;Gabriel Kuhn;Liang Peng

  • Using a bootstrap method to choose the sample fraction in tail index estimation

    J. Daníelsson;L.F.M. deHaan;L. Peng;C.G. deVries

Frequent Co-Authors

Qiwei Yao
Qiwei Yao London School of Economics and Political Science
Claudia Klüppelberg
Claudia Klüppelberg Technical University of Munich
Song Xi Chen
Song Xi Chen Peking University
Laurens de Haan
Laurens de Haan Erasmus University Rotterdam
Shiqing Ling
Shiqing Ling Hong Kong University of Science and Technology
Alan H. Welsh
Alan H. Welsh Australian National University
Bing-Yi Jing
Bing-Yi Jing Hong Kong University of Science and Technology
Jon Danielsson
Jon Danielsson London School of Economics and Political Science
Martin T. Wells
Martin T. Wells Cornell University

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 interested in Mathematics, exploring related online degrees can open doors to diverse career opportunities. Many graduates combine their strong analytical skills with business acumen by pursuing programs like the cheapest online master's in finance, which offers a cost-effective route to advanced financial expertise.

Accelerated online programs have gained popularity for their flexibility and efficiency. Those seeking leadership roles can consider accelerated mba programs online, designed to fast-track business education without compromising quality. Similarly, one year mba programs offer focused curriculums that fit well with an already busy schedule.

For individuals drawn to the digital marketplace, an ms in digital marketing degree cost usa provides both affordability and lucrative career prospects. Integrating mathematics with marketing analytics can lead to high-demand roles in data-driven marketing strategies.

Overall, combining mathematical training with these complementary online degrees can enhance career trajectories and offer competitive advantages in diverse sectors such as finance, marketing, and management.

Best Scientists Citing Liang Peng

Trending Scientists

Recently Published Articles