World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
41
Citations
10621
World Ranking
1867
National Ranking
129

Qiwei Yao 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 Qiwei Yao 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: 154 publications — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Qiwei Yao 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 Qiwei Yao 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: 41 D-Index — 49th percentile

49% 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

  • 2009 - Fellow of the American Statistical Association (ASA)

Overview

Qiwei Yao is affiliated with the London School of Economics and Political Science in the United Kingdom. Their research primarily focuses on the field of Computer Science, with a particular emphasis on subfields such as Signal Processing, Economics and Econometrics, Statistics and Probability, Artificial Intelligence, and Management Science and Operations Research.

The scientist's work addresses several main topics including:

  • Statistical Methods and Inference
  • Time Series Analysis and Forecasting
  • Complex Systems and Time Series Analysis
  • Blind Source Separation Techniques
  • Forecasting Techniques and Applications
  • Financial Risk and Volatility Modeling
  • Tensor decomposition and applications

Yao has contributed to multiple research papers, some of the recent notable publications are:

  • "A novel in-situ sensor calibration method for building thermal systems based on virtual samples and autoencoder" (2024), published in Energy
  • "Modelling matrix time series via a tensor CP-decomposition" (2023), published in Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • "Estimation of Subgraph Densities in Noisy Networks" (2020), published in Journal of the American Statistical Association
  • "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression" (2021), published in Applied Energy
  • "An autocovariance-based learning framework for high-dimensional functional time series" (2023), published in Journal of Econometrics

Yao's frequent co-authors include Jinyuan Chang, Zhe Sun, Xinghao Qiao, Huaqiang Jin, and Yingjie Xu. This reflects a collaboration pattern across multiple research efforts and publications.

The scientist has regularly published in several academic venues, with multiple papers appearing in:

  • arXiv (Cornell University)
  • Journal of the American Statistical Association
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • Journal of Econometrics
  • SSRN Electronic Journal

Qiwei Yao was recognized as a Fellow of the American Statistical Association (ASA) in 2009, indicating a distinguished status within the professional statistical community.

Best Publications

  • Nonlinear Time Series: Nonparametric and Parametric Methods

    Jianqing Fan;Qiwei Yao

  • Functional-Coefficient Regression Models for Nonlinear Time Series

    Zongwu Cai;Jianqing Fan;Qiwei Yao

  • Efficient Estimation of Conditional Variance Functions in Stochastic Regression

    Jianqing Fan;Qiwei Yao

  • Inference in ARCH and GARCH models with heavy-tailed errors

    Peter Hall;Qiwei Yao

  • Methods for estimating a conditional distribution function

    Peter Hall;Rodney C. L. Wolff;Qiwei Yao

  • Factor modeling for high-dimensional time series: inference for the number of factors

    Clifford Lam;Qiwei Yao

  • Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems

    Jianqing Fan;Qiwei Yao;Howell Tong

  • Adaptive varying-coefficient linear models

    Jianqing Fan;Qiwei Yao;Zongwu Cai

  • Modelling multiple time series via common factors

    Jiazhu Pan;Qiwei Yao

  • Least absolute deviations estimation for ARCH and GARCH models

    Liang Peng;Qiwei Yao

  • Estimation of latent factors for high-dimensional time series

    Clifford Lam;Qiwei Yao;Neil Bathia

  • Asymmetric least squares regression estimation: a nonparametric approach

    Qiwei Yao;Howell Tong

  • Nonparametric estimation and symmetry tests for conditional density functions

    Robert J Hyndman;Qiwei Yao

  • Linearity testing using local polynomial approximation

    Vidar Hjellvik;Qiwei Yao;Dag Tjøstheim

  • Modeling and forecasting daily electricity load curves: a hybrid approach

    Haeran Cho;Yannig Goude;Xavier Brossat;Qiwei Yao

  • Tests for change-points with epidemic alternatives

    Qiwei Yao

  • Quantifying the Influence of Initial Values on Non‐Linear Prediction

    Qiwei Yao;Howell Tong

  • Large Volatility Matrix Inference via Combining Low-Frequency and High-Frequency Approaches

    Minjing Tao;Yazhen Wang;Qiwei Yao;Jian Zou

  • Modelling multivariate volatilities via conditionally uncorrelated components

    Jianqing Fan;Mingjin Wang;Qiwei Yao

  • Methods for estimating a conditional distribution function

    Rodney C Wolff;Peter Hall;Qiwei Yao

  • Adaptive Varying-Coefficient Linear Models

    Zongwu Cai;Jianqin Fan;Qiwei Yao

Frequent Co-Authors

Howell Tong
Howell Tong London School of Economics and Political Science
Jianqing Fan
Jianqing Fan Princeton University
Liang Peng
Liang Peng Georgia State University
Dag Tjøstheim
Dag Tjøstheim University of Bergen
Hansheng Wang
Hansheng Wang Peking University
Nils Chr. Stenseth
Nils Chr. Stenseth University of Oslo
Peter J. Brockwell
Peter J. Brockwell Colorado State University
Carl Chiarella
Carl Chiarella University of Technology Sydney
Stefan Mittnik
Stefan Mittnik Ludwig-Maximilians-Universität München
Olivier Scaillet
Olivier Scaillet University of Geneva

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