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Jane-Ling Wang

D-Index & Metrics

Mathematics

D-Index
32
Citations
6606
World Ranking
3127
National Ranking
1253

Jane-Ling Wang 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 Jane-Ling Wang 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: 92 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Jane-Ling Wang 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 Jane-Ling Wang 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: 32 D-Index — 14th percentile

14% 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

  • 2010 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Jane-Ling Wang is affiliated with the University of California, Davis in the United States. Their research is primarily situated within the field of Mathematics, with a strong focus on Statistics and Probability. The scope of their work extends to multiple subfields, including Artificial Intelligence, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, and Health, Toxicology and Mutagenesis.

Their research covers a range of topics, notably:

  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Functional Brain Connectivity Studies
  • Advanced Neuroimaging Techniques and Applications
  • Machine Learning and Data Classification
  • Health, Environment, Cognitive Aging
  • Metabolomics and Mass Spectrometry Studies

Jane-Ling Wang has contributed to several peer-reviewed publication venues. Frequent venues include:

  • arXiv (Cornell University)
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • Biometrika
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Neural Networks

Their recent papers demonstrate a range of interdisciplinary research interests and cover various aspects of statistical methodology and application in artificial intelligence and neuroscience. Selected recent publications include:

  • "ML-LOO: Detecting Adversarial Examples with Feature Attribution," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Emotional EEG classification using connectivity features and convolutional neural networks," 2020, Neural Networks
  • "Mountaineers on Mount Everest: Effects of age, sex, experience, and crowding on rates of success and death," 2020, PLoS ONE
  • "Deep learning for the partially linear Cox model," 2022, The Annals of Statistics
  • "Mean and Covariance Estimation for Functional Snippets," 2020, OPAL (Open@LaTrobe) (La Trobe University)

Frequent collaborators in Jane-Ling Wang's research include:

  • Qixian Zhong
  • Yaqing Chen
  • Hans-Georg Müller
  • Muriel Bruchhage
  • Sean Deoni

Among the recognitions received, Jane-Ling Wang was named a Fellow of the American Association for the Advancement of Science (AAAS) in 2010.

Best Publications

  • Functional Data Analysis for Sparse Longitudinal Data

    Fang Yao;Hans-Georg Müller;Jane-Ling Wang

  • Functional Data Analysis

    Jane-Ling Wang;Jeng-Min Chiou;Hans-Georg Müller

  • Functional linear regression analysis for longitudinal data

    Fang Yao;Hans-Georg Müller;Jane-Ling Wang

  • Properties of principal component methods for functional and longitudinal data analysis

    Peter Hall;Hans-Georg Müller;Jane-Ling Wang

  • Hazard rate estimation under random censoring with varying kernels and bandwidths.

    Hans-Georg Muller;Jane-Ling Wang

  • Joint modeling of survival and longitudinal data : Likelihood approach revisited

    Fushing Hsieh;Yi Kuan Tseng;Jane Ling Wang

  • From sparse to dense functional data and beyond

    Xiaoke Zhang;Jane-Ling Wang

  • Estimation for a partial-linear single-index model

    Jane Ling Wang;Liugen Xue;Lixing Zhu;Yun Sam Chong

  • Joint modelling of accelerated failure time and longitudinal data

    Yi-Kuan Tseng;Fushing Hsieh;Jane-Ling Wang

  • Density and hazard rate estimation for censored data via strong representation of the Kaplan-Meier estimator

    S. H. Lo;Y. P. Mack;J. L. Wang

  • FUNCTIONAL RESPONSE MODELS

    Jeng-Min Chiou;Hans-Georg Muller;Jane-Ling Wang

  • Strong Representations of the Survival Function Estimator for Truncated and Censored Data with Applications

    I. Gijbels;J.L. Wang

  • Functional canonical analysis for square integrable stochastic processes

    Guozhong He;Hans-Georg Müller;Jane-Ling Wang

  • Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data.

    Jimin Ding;Jane-Ling Wang

  • Review of Functional Data Analysis

    Jane-Ling Wang;Jeng-Min Chiou;Hans-Georg Mueller

  • Functional quasi-likelihood regression models with smooth random effects

    Jeng-Min Chiou;Hans-Georg Müller;Jane-Ling Wang

  • Robust functional principal components: A projection-pursuit approach

    Juan Lucas Bali;Graciela Lina Boente Boente;David E. Tyler;Jane Ling Wang

  • Dimension reduction for censored regression data

    Ker-Chau Li;Jane-Ling Wang;Chun-Houh Chen

  • COVARIATE ADJUSTED FUNCTIONAL PRINCIPAL COMPONENTS ANALYSIS FOR LONGITUDINAL DATA

    Ci-Ren Jiang;Jane-Ling Wang

  • Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

    Puyudi Yang;Jianbo Chen;Cho-Jui Hsieh;Jane-Ling Wang

  • Locally adaptive hazard smoothing

    Hans-Georg Müller;Jane-Ling Wang

  • Analysis of oldest-old mortality: lifetables revisited

    Jane-Ling Wang;Hans-Georg Müller;William B. Capra

  • A comparison of hazard rate estimators for left truncated and right censored data

    Unknown

  • Methods of canonical analysis for functional data

    Guozhong He;Hans-Georg Müller;Jane-Ling Wang

  • A FUNCTIONAL MULTIPLICATIVE EFFECTS MODEL FOR LONGITUDINAL DATA, WITH APPLICATION TO REPRODUCTIVE HISTORIES OF FEMALE MEDFLIES.

    Jeng-Min Chiou;Hans-Georg Müller;Jane-Ling Wang;James R. Carey

  • Spontaneous neural fluctuations predict decisions to attend

    Jesse J. Bengson;Todd A. Kelley;Xiaoke Zhang;Jane-Ling Wang

  • Two-Sample Inference for Median Survival Times Based on One-Sample Procedures for Censored Survival Data

    Jane-Ling Wang;Thomas P. Hettmansperger

  • Robust functional principal components: A projection-pursuit approach

    Juan Lucas Bali;Graciela Boente;David E. Tyler;Jane-Ling Wang

  • Functional linear regression via canonical analysis

    Guozhong He;Hans-Georg Müller;Jane-Ling Wang;Wenjing Yang

  • Nonparametric Regression Analysis of Longitudinal Data

    J. ‐L. Wang

Frequent Co-Authors

Hans-Georg Müller
Hans-Georg Müller University of California, Davis
James R. Carey
James R. Carey University of California, Davis
Nikos T. Papadopoulos
Nikos T. Papadopoulos University Of Thessaly
Winfried Stute
Winfried Stute University of Giessen
Linda Partridge
Linda Partridge Max Planck Society
James W. Vaupel
James W. Vaupel University of Southern Denmark
Richard M. Martin
Richard M. Martin University of Bristol
Lixing Zhu
Lixing Zhu Beijing Normal University
Sean C.L. Deoni
Sean C.L. Deoni Brown University
Raymond B. Huey
Raymond B. Huey University of Washington

External Links

Wikipedia of Jane-Ling Wang Personal website of Jane-Ling Wang

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Best Scientists Citing Jane-Ling Wang

Hans-Georg Müller

Hans-Georg Müller

University of California, Davis

Publications: 86

James R. Carey

James R. Carey

University of California, Davis

Publications: 70

Nikos T. Papadopoulos

Nikos T. Papadopoulos

University Of Thessaly

Publications: 32

Piotr Kokoszka

Piotr Kokoszka

Colorado State University

Publications: 21

Anatoli I. Yashin

Anatoli I. Yashin

Duke University

Publications: 21

Hua Liang

Hua Liang

George Washington University

Publications: 19

Lixing Zhu

Lixing Zhu

Beijing Normal University

Publications: 19

Raymond J. Carroll

Raymond J. Carroll

Texas A&M University

Publications: 17

Winfried Stute

Winfried Stute

University of Giessen

Publications: 15

David Ruppert

David Ruppert

Cornell University

Publications: 14

James W. Vaupel

James W. Vaupel

University of Southern Denmark

Publications: 14

Ingrid Van Keilegom

Ingrid Van Keilegom

KU Leuven

Publications: 13

Joseph G. Ibrahim

Joseph G. Ibrahim

University of North Carolina at Chapel Hill

Publications: 12

Lajos Horváth

Lajos Horváth

University of Utah

Publications: 11

Linda Partridge

Linda Partridge

Max Planck Society

Publications: 11

Thomas W. Scott

Thomas W. Scott

University of California, Davis

Publications: 11

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