Tiejun Tong, PhD
Department of Mathematics
Hong Kong Baptist University
Kowloon Tong, Hong Kong
Office: FSC1201, Fong Shu Chuen Building
Phone: (852) 3411-7340
Fax: (852) 3411-5811
2013- Associate Professor, Department of Mathematics, Hong Kong Baptist University
2010-2013 Assistant Professor, Department of Mathematics, Hong Kong Baptist University
2007-2011 Assistant Professor, Department of Applied Mathematics, University of Colorado at Boulder (on leave during 2010-2011)
2005-2007 Postdoctoral Associate, Department of Biostatistics, Yale University
PhD in Statistics (2005), Department of Statistics and Applied Probability, University of California at Santa Barbara
MSc in Statistics (2001), Department of Statistics and Finance, University of Science and Technology of China
BSc in Electronic Engineering (1998), School of the Gifted Young, University of Science and Technology of China
Research Interests and Publications
(Papers by Year,
Medical statistics and meta-analysis (Papers)
High-dimensional data analysis (Papers)
Nonparametric and semiparametric regression (Papers)
Current and Former Students
Co-Guest Editor, Special Issue on "Modern Meta-Analysis and Network Meta-Analysis", Statistics and Its Interface
Associate Editor, Statistics and Its Interface, 2017-present
Review Editor, Frontiers in Bioinformatics and Computational Biology, 2016-present
Honors and Awards
Math Citation Award, Hong Kong Baptist University, 2018, 2017, 2016
Guest Professor, Zhejiang Gongshang University, 2015
Elected Member, International Statistical Institute, 2009-present
Junior Faculty Development Award, University of Colorado at Boulder, 2009
Ruth and Joe Gani Prize, University of California at Santa Barbara, 2006
Abraham Wald Memorial Prize, University of California at Santa Barbara, 2002
Three Most Representative Publications
Dehui Luo, Xiang Wan, Jiming Liu and Tiejun Tong (2018).
Optimally estimating the sample mean from the sample size, median, mid-range and/or mid-quartile range.
Statistical Methods in Medical Research, 27: 1785-1805.
-- This paper establishes a new "rule of thumb" between the sample mean, median and mid-range.
It has received 9 citations in ISI Web of Science by 12/12/2018 (as well as 25 citations in Google Scholar) and has been recognized as a highly cited paper.
It is also worth noting that the paper was recognized as a "great manuscript" by the referees, with typical comments as follows:
"From practical evidence-based medicine perspective, the optimal estimation of sample mean with analytical form of weight given, can be readily applied in everyday data analysis process, e.g. random effect meta-analysis."
Wenlin Dai, Tiejun Tong and Lixing Zhu (2017).
On the choice of difference sequence in a unified framework for variance estimation in nonparametric regression.
Statistical Science, 32: 455-468.
-- As quoted from the abstract of the paper, the main contributions of this paper are as follows:
"The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades.".
The paper is expected to become a classic paper in the literature of difference-based methods in nonparametric regression.
Xiang Wan, Wenqian Wang, Jiming Liu and Tiejun Tong (2014).
Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range.
BMC Medical Research Methodology, 14: 135.
-- This paper establishes an improved "rule of thumb" between the sample range and the sample standard deviation.
The paper has been recognized as a highly cited paper (and also a hot paper in 2016) in ISI Web of Science with a total of 390 citations, including 5 citations in 2015, 55 citations in 2016, 134 citations in 2017, and 196 citations in 2018 by 12/12/2018.
The paper has also received many thank-you letters from all over the world.
In addition to show their appreciation, they also claimed that our paper will be very helpful and important to improve patient lives, and will influence decision making in medical practice.
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