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Mathematics

D-Index
37
Citations
8538
World Ranking
2439
National Ranking
95

Research.com Recognitions

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

Overview

Jiahua Chen is affiliated with the University of British Columbia in Canada. Their research spans multiple fields with a strong focus on mathematics and computer science, particularly in statistics and probability, artificial intelligence, cancer research, molecular biology, and immunology.

The scientist has contributed broadly to topics including Bayesian methods and mixture models, statistical methods and Bayesian inference, statistical methods and inference, statistical distribution estimation and applications, cancer-related molecular mechanisms research, Gaussian processes and Bayesian inference, and advanced statistical methods and models.

Some frequent coauthors in Chen's collaborations are Archer Gong Zhang, Junhui Zhang, Bao Guo, Lei Zhan, and Yunxia Cao.

The publication record includes papers in various venues, most notably arXiv (Cornell University) with 11 publications, International Journal of Gynecological Cancer and Canadian Journal of Statistics with 2 publications each, Scientific Reports with 2 publications, and the Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA) with 2 publications.

Selected recent papers include:

  • Comparison of laparoscopic and open radical hysterectomy in cervical cancer patients with tumor size ≤2 cm, 2020, International Journal of Gynecological Cancer
  • Identification and validation of a six-gene signature associated with glycolysis to predict the prognosis of patients with cervical cancer, 2020, BMC Cancer
  • METTL3 facilitates immunosurveillance by inhibiting YTHDF2-mediated NLRC5 mRNA degradation in endometrial cancer, 2023, Biomarker Research
  • Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province, 2023, Frontiers in Plant Science
  • Recent Insights into Noncoding RNAs in Primary Ovarian Insufficiency: Focus on Mechanisms and Treatments, 2023, The Journal of Clinical Endocrinology & Metabolism

Chen has also authored books published by Springer Nature, including "Statistical Inference Under Mixture Models" (2023) and "Advances and Innovations in Statistics and Data Science" (2022).

Among professional recognitions, Chen was named a Fellow of the American Statistical Association (ASA) in 2009.

Best Publications

  • Extended Bayesian information criteria for model selection with large model spaces

    Jiahua Chen;Zehua Chen

  • Nearest Neighbor Imputation for Survey Data

    Jiahua Chen;Jun Shao

  • A catalogue of two-level and three-level fractional factorial designs with small runs

    Jiahua Chen;D. X. Sun;C. F. J. Wu

  • A modified likelihood ratio test for homogeneity in finite mixture models

    Hanfeng Chen;Jiahua Chen;John D. Kalbfleisch

  • Empirical likelihood estimation for finite populations and the effective usage of auxiliary information

    Jiahua Chen;Jing Qin

  • Variable Selection in Finite Mixture of Regression Models

    Abbas Khalili;Jiahua Chen

  • Optimal Rate of Convergence for Finite Mixture Models

    Jiahua Chen

  • A PSEUDO EMPIRICAL LIKELIHOOD APPROACH TO THE EFFECTIVE USE OF AUXILIARY INFORMATION IN COMPLEX SURVEYS

    Jiahua Chen;R. R. Sitter

  • EXTENDED BIC FOR SMALL-n-LARGE-P SPARSE GLM

    Jiahua Chen;Zehua Chen

  • Adjusted Empirical Likelihood and its Properties

    Jiahua Chen;Asokan Mulayath Variyath;Bovas Abraham

  • Hypothesis test for normal mixture models: The EM approach

    Jiahua Chen;Pengfei Li

  • Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys

    J. Chen;R. R. Sitter;C. Wu

  • Testing for a finite mixture model with two components

    Hanfeng Chen;Jiahua Chen;John D. Kalbfleisch

  • Jackknife Variance Estimation for Nearest-Neighbor Imputation

    Jiahua Chen;Jun Shao

  • Penalized minimum‐distance estimates in finite mixture models

    Jiahua Chen;J.D. Kalbfleisch

  • Fractional resolution and minimum aberration in blocked 2 n−k designs

    Randy R. Sitter;Jiahua Chen;Moshe Feder

  • INFERENCE FOR NORMAL MIXTURES IN MEAN AND VARIANCE

    Jiahua Chen;Xianming Tan;Runchu Zhang

  • The likelihood ratio test for homogeneity in finite mixture models

    Hanfeng Chen;Jiahua Chen

  • Some Results on $2^{n - k}$ Fractional Factorial Designs and Search for Minimum Aberration Designs

    Jiahua Chen

  • Non-finite Fisher information and homogeneity: an em approach

    P. Li;J. Chen;P. Marriott

Frequent Co-Authors

John D. Kalbfleisch
John D. Kalbfleisch University of Michigan–Ann Arbor
James V. Zidek
James V. Zidek University of British Columbia
J. N. K. Rao
J. N. K. Rao Carleton University
Dennis K. J. Lin
Dennis K. J. Lin Purdue University West Lafayette
Jun Shao
Jun Shao University of Wisconsin–Madison
Rollin Brant
Rollin Brant University of British Columbia
Jeffrey A. Lieberman
Jeffrey A. Lieberman Columbia University
Martin Styner
Martin Styner University of North Carolina at Chapel Hill
Bing-Yi Jing
Bing-Yi Jing Hong Kong University of Science and Technology

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