World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

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

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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