World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
49
Citations
11925
World Ranking
1130
National Ranking
61

Research.com Recognitions

  • 2001 - Fellow of the American Statistical Association (ASA)

Overview

Kai-Tai Fang is a researcher affiliated with Beijing Normal University in China. Their work spans the fields of Computer Science and Mathematics, with a particular focus on Statistics and Probability, Analytical Chemistry, and Artificial Intelligence. Their research integrates topics such as Spectroscopy and Chemometric Analyses, Statistical Methods and Inference, Statistical Distribution Estimation and Applications, Probabilistic and Robust Engineering Design, Advanced Statistical Methods and Models, Face and Expression Recognition, and Machine Learning and Data Classification.

The scientist has produced publications in several notable venues. These include the Journal of Data Science, where they have two publications; Mathematics; Communication in Statistics - Theory and Methods; Entropy; and UNC Libraries.

Recent papers by Kai-Tai Fang cover diverse aspects of statistical and computational methods. These include:

  • Boosting Applied to Classification of Mass Spectral Data, 2021, Journal of Data Science
  • Representative Points from a Mixture of Two Normal Distributions, 2022, Mathematics
  • The Classification Tree Combined with SIR and Its Applications to Classification of Mass Spectra, 2021, Journal of Data Science
  • Limiting Behavior of the Gap Between the Largest Two Representative Points of Statistical Distributions, 2021, Communication in Statistics - Theory and Methods
  • The Representative Points of Generalized Alpha Skew-t Distribution and Applications, 2024, Entropy

Kai-Tai Fang has collaborated frequently with a number of coauthors. These include A. M. Elsawah, Barkahoum Laala, Gajendra K. Vishwakarma, Курт Вармуза, and Ping He.

Among the scientist's recognitions is their election as a Fellow of the American Statistical Association (ASA) in 2001.

Best Publications

  • Symmetric Multivariate and Related Distributions

    Kai-Tai Fang;Samuel Kotz;Kai Wang Ng

  • Uniform Design: Theory and Application

    Kai Tai Fang;Dennis K.J. Lin;Peter Winker;Yong Zhang

  • The Meta-elliptical Distributions with Given Marginals

    Hong-Bin Fang;Kai-Tai Fang;Samuel Kotz

  • Uniform design and its applications in chemistry and chemical engineering

    Yi-zeng Liang;Kai-tai Fang;Qing-song Xu

  • Asymptotics for kernel estimate of sliced inverse regression

    Lixing Zhu;Kai Tai Fang

  • Maximum Likelihood Estimation

    Jian-Xin Pan;Kai-Tai Fang

  • The effective dimension and quasi-Monte Carlo integration

    Xiaoqun Wang;Kai-Tai Fang

  • Statistical inference in elliptically contoured and related distributions

    Kai-Tang Fang;T. W. Anderson

  • Centered L 2 -discrepancy of random sampling and Latin hypercube design, and construction of uniform designs

    Kai-Tai Fang;Chang-Xing Ma;Peter Winker

  • Ch. 4. Uniform experimental designs and their applications in industry

    Kai Tai Fang;Dennis K.J. Lin

  • Monte Carlo and Quasi-Monte Carlo Methods 2000

    Kaitai T. Fang;H. Niederreiter;F. J. Hickernell

  • Application of Threshold-Accepting to the Evaluation of the Discrepancy of a Set of Points

    Peter Winker;Kai-Tai Fang

  • Theory and Application of Uniform Experimental Designs

    Kai-Tai Fang;Min-Qian Liu;Hong Qin;Yong-Dao Zhou

  • A note on generalized aberration in factorial designs

    Chang-Xing Ma;Kai-Tai Fang

  • A connection between uniformity and aberration in regular fractions of two-level factorials

    Kai-Tai Fang;Rahul Mukerjee

  • Maximum-likelihood estimates and likelihood-ratio criteria for multivariate elliptically contoured distributions

    T. W. Anderson;Huang Hsu;Kai-Tai Fang

  • Optimal mixed-level supersaturated design

    Kai Tai Fang;Dennis K.J. Lin;Min Qian Liu

  • Lower bounds for centered and wrap-around L 2 -discrepancies and construction of uniform designs by threshold accepting

    Kai-Tai Fang;Xuan Lu;Peter Winker

  • Design and Modeling for Computer Experiments (Computer Science & Data Analysis)

    Kai-Tai Fang;Runze Li;Agus Sudjianto

  • Mixture discrepancy for quasi-random point sets

    Yong-Dao Zhou;Kai-Tai Fang;Kai-Tai Fang;Jian-Hui Ning

  • Some Applications of Number-Theoretic Methods in Statistics

    Kai-Tai Fang;Yuan Wang;Peter M. Bentler

Frequent Co-Authors

Yi-Zeng Liang
Yi-Zeng Liang Central South University
Runze Li
Runze Li Pennsylvania State University
Dennis K. J. Lin
Dennis K. J. Lin Purdue University West Lafayette
Gennian Ge
Gennian Ge Capital Normal University
Samuel Kotz
Samuel Kotz George Washington University
Rahul Mukerjee
Rahul Mukerjee Indian Institute of Management Calcutta
Fred J. Hickernell
Fred J. Hickernell Illinois Institute of Technology
Qing-Song Xu
Qing-Song Xu Central South University
Peter M. Bentler
Peter M. Bentler University of California, Los Angeles
Lixing Zhu
Lixing Zhu Beijing Normal 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 pursuing Mathematics in the USA, expanding skill sets with related online degrees can open diverse career pathways. Many mathematics graduates find success in fields like marketing, business administration, and data analytics, where analytical thinking is highly valued.

Those interested in combining their quantitative skills with business acumen might explore affordable options such as a masters in marketing. This degree blends creativity with data-driven strategies, enhancing job prospects in competitive markets.

Another popular advancement is pursuing an MBA to deepen leadership and management skills. For busy professionals, a one year MBA program offers an efficient path to career growth without extended time commitments. Additionally, many online programs facilitate smoother transitions through options like mba transfer credits, helping students leverage prior coursework.

Those focused on data-driven decision-making may consider an ms in data analytics. This degree complements mathematics by training students to interpret complex datasets, an essential skill across industries including finance, technology, and healthcare.

Best Scientists Citing Kai-Tai Fang

Trending Scientists