World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
40
Citations
22621
World Ranking
1972
National Ranking
831

Chih-Ling Tsai 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 Chih-Ling Tsai 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: 135 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Chih-Ling Tsai 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 Chih-Ling Tsai 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: 40 D-Index — 45th percentile

45% 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

  • 2015 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 1996 - Fellow of the American Statistical Association (ASA)

Overview

Chih-Ling Tsai is affiliated with the University of California, Davis in the United States. Their academic contributions span fields primarily including Mathematics and Economics, Econometrics and Finance.

Their research covers several subfields such as Statistics and Probability, Economics and Econometrics, Statistical and Nonlinear Physics, Computer Networks and Communications, and Experimental and Cognitive Psychology. The main topics of their work involve Spatial and Panel Data Analysis, Complex Network Analysis Techniques, Advanced Statistical Methods and Models, Statistical Methods and Bayesian Inference, Statistical Methods and Inference, Random Matrices and Applications, and Mental Health Research Topics.

Frequent publication venues for Tsai include arXiv (Cornell University), Journal of Business and Economic Statistics, Journal of Econometrics, Computational Statistics & Data Analysis, and Journal of the American Statistical Association.

Recent papers authored or coauthored include:

  • Community influence analysis in social networks, 2024, Computational Statistics & Data Analysis
  • Covariance Model with General Linear Structure and Divergent Parameters, 2022, Journal of Business and Economic Statistics
  • Inward and Outward Network Influence Analysis, 2021, Journal of Business and Economic Statistics
  • Imputations for High Missing Rate Data in Covariates Via Semi-supervised Learning Approach, 2021, Journal of Business and Economic Statistics
  • Inference on covariance-mean regression, 2021, Journal of Econometrics

Tsai often collaborates with several researchers, including Wei Lan, Tao Zou, Xinyan Fan, and Ronghua Luo, with the most frequent coauthors being Wei Lan and Tao Zou.

The scientist has been recognized as a Fellow of the American Association for the Advancement of Science (AAAS) since 2015 and as a Fellow of the American Statistical Association (ASA) since 1996.

Best Publications

  • Regression and time series model selection in small samples

    Clifford M. Hurvich;Chih Ling Tsai

  • Improved Methods for Tests of Long-Run Abnormal Stock Returns

    John D. Lyon;Brad M. Barber;Chih-Ling Tsai

  • Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion

    Clifford M. Hurvich;Jeffrey S. Simonoff;Chih‐Ling Tsai

  • Regression and Time Series Model Selection

    Allan D R McQuarrie;Chih-Ling Tsai

  • Tuning parameter selectors for the smoothly clipped absolute deviation method.

    Hansheng Wang;Runze Li;Chih Ling Tsai

  • Linear regression

    Unknown

  • Improved Methods for Tests of Long-Run Abnormal Stock Returns

    Brad M. Barber;Chih-Ling Tsai

  • A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION

    Clifford M. Hurvich;Chih-Ling Tsai

  • Subgroup Analysis via Recursive Partitioning

    Xiaogang Su;Chih-Ling Tsai;Hansheng Wang;David M. Nickerson

  • Bias of the corrected AIC criterion for underfitted regression and time series models

    Clifford M. Hurvich;Chih-Ling Tsai

  • The impact of model selection on inference in linear regression

    Clifford M. Hurvich;Chih-Ling Tsai

  • Model selection for extended quasi-likelihood models in small samples

    Clifford M. Hurvich;Chih-Ling Tsai

  • Regression coefficient and autoregressive order shrinkage and selection via the lasso

    Hansheng Wang;Guodong Li;Chih-Ling Tsai

  • Regularization Parameter Selections via Generalized Information Criterion

    Yiyun Zhang;Runze Li;Chih Ling Tsai

  • MODEL SELECTION FOR MULTIVARIATE REGRESSION IN SMALL SAMPLES

    Edward J. Bedrick;Chih-Ling Tsai

  • ESTIMATION AND TESTING FOR PARTIALLY LINEAR SINGLE-INDEX MODELS.

    Hua Liang;Xiang Liu;Runze Li;Chih Ling Tsai

  • Quantile correlations and quantile autoregressive modeling

    Guodong Li;Yang Li;Chih-Ling Tsai

  • Markov-switching model selection using Kullback–Leibler divergence

    Aaron D. Smith;Prasad A. Naik;Chih-Ling Tsai;Chih-Ling Tsai

  • Bias in nonlinear regression

    R. D. Cook;C.-L. Tsai;B. C. Wei

  • Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples

    Clifford M. Hurvich;Robert Shumway;Chih-Ling Tsai

  • Regression model selection—a residual likelihood approach

    Peide Shi;Chih-Ling Tsai

Frequent Co-Authors

Hansheng Wang
Hansheng Wang Peking University
Clifford M. Hurvich
Clifford M. Hurvich New York University
Jeffrey S. Simonoff
Jeffrey S. Simonoff New York University
Runze Li
Runze Li Pennsylvania State University
Lexin Li
Lexin Li University of California, Berkeley
R. Dennis Cook
R. Dennis Cook University of Minnesota
Hua Liang
Hua Liang George Washington University
Brad M. Barber
Brad M. Barber University of California, Davis
Liangjun Su
Liangjun Su Tsinghua University
Anthony C. Davison
Anthony C. Davison École Polytechnique Fédérale de Lausanne

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