Tiejun Tong

16.7k total citations · 4 hit papers
106 papers, 11.9k citations indexed

About

Tiejun Tong is a scholar working on Statistics and Probability, Molecular Biology and Statistics, Probability and Uncertainty. According to data from OpenAlex, Tiejun Tong has authored 106 papers receiving a total of 11.9k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Statistics and Probability, 27 papers in Molecular Biology and 13 papers in Statistics, Probability and Uncertainty. Recurrent topics in Tiejun Tong's work include Statistical Methods and Inference (33 papers), Gene expression and cancer classification (19 papers) and Advanced Statistical Methods and Models (18 papers). Tiejun Tong is often cited by papers focused on Statistical Methods and Inference (33 papers), Gene expression and cancer classification (19 papers) and Advanced Statistical Methods and Models (18 papers). Tiejun Tong collaborates with scholars based in Hong Kong, China and United States. Tiejun Tong's co-authors include Xiang Wan, Jiming Liu, Wenqian Wang, Dehui Luo, Jiandong Shi, Xian‐Tao Zeng, Hong Weng, Haitao Chu, Lu Lin and Hongyu Zhao and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Journal of the American Statistical Association.

In The Last Decade

Tiejun Tong

95 papers receiving 11.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

2014 7.8k citations Xiang Wan, Wenqian Wang et al. BMC Medical Research Methodology profile →
Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range

2016 2.6k citations Dehui Luo, Xiang Wan et al. profile →
Optimally estimating the sample standard deviation from the five‐number summary

2020 400 citations Dehui Luo, Hong Weng et al. Research Synthesis Methods profile →
Detecting the skewness of data from the five-number summary and its application in meta-analysis

2023 162 citations Dehui Luo, Xiang Wan et al. profile →

Peers

Countries citing papers authored by Tiejun Tong

Since Specialization

Citations

This map shows the geographic impact of Tiejun Tong's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tiejun Tong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tiejun Tong more than expected).

Fields of papers citing papers by Tiejun Tong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tiejun Tong. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tiejun Tong. The network helps show where Tiejun Tong may publish in the future.

Co-authorship network of co-authors of Tiejun Tong

This figure shows the co-authorship network connecting the top 25 collaborators of Tiejun Tong. A scholar is included among the top collaborators of Tiejun Tong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tiejun Tong. Tiejun Tong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Robust and Efficient Mediation Analysis via Huber Loss 2025 ·Psychometrika ·Wenwu Wang, Xinsheng Peng, Tiejun Tong	1
2	Development, validation and visualization of a web-based nomogram for predicting risk of new-onset diabetes after percutaneous coronary intervention 2024 ·Scientific Reports ·Mengmeng Zhu, Yiwen Li, Wenting Wang, Yanfei Liu, Tiejun Tong, Yue Liu	0
3	Elevated extracellular matrix protein 1 in circulating extracellular vesicles supports breast cancer progression under obesity conditions 2024 ·Nature Communications ·Keyang Xu, Ai Fu, (unknown), (unknown), (unknown), (unknown), (unknown), Tao Su, Tiejun Tong, Jianfeng Bao, Aiping Lyu, Hiu Yee Kwan	10
4	Efficacy and safety of Chinese patent medicine Xiao Yao San in polycystic ovary syndrome: A systematic review and meta-analysis 2023 ·Journal of Ethnopharmacology ·Xuan Zhou, Qingyu Ma, (unknown), (unknown), (unknown), Jia-Jia Qin, Tiejun Tong, (unknown), Yongxin Li, Yaping Wang, Jiaxu Chen	5
5	scDLC: a deep learning framework to classify large sample single-cell RNA-seq data 2022 ·BMC Genomics ·Yan Zhou, (unknown), Bin Yang, Tiejun Tong, Baoxue Zhang, Niansheng Tang	8
6	Estimating the reference interval from a fixed effects meta‐analysis 2021 ·Research Synthesis Methods ·Wenhao Cao, Lianne Siegel, Jincheng Zhou, Motao Zhu, Tiejun Tong, Yong Chen, Haitao Chu	4
7	Optimally estimating the sample mean and standard deviation from the five-number summary 2020 ·arXiv (Cornell University) ·(unknown), Dehui Luo, Hong Weng, Xian‐Tao Zeng, Lu Lin, Haitao Chu, Tiejun Tong	3
8	Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression 2019 ·Journal of Machine Learning Research ·(unknown), Ping Yu, Lu Lin, Tiejun Tong	8
9	Optimal difference-based estimation for partially linear models 2017 ·Computational Statistics ·(unknown), (unknown), Wenlin Dai, Tiejun Tong	0
10	A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix 2017 ·The International Journal of Biostatistics ·(unknown), (unknown), Wenlin Dai, Tiejun Tong	1
11	如何实现从样本量、中位数、极值或四分位数到均数与标准差的转换 2017 ·(unknown), (unknown), (unknown), Tiejun Tong	6
12	Optimal estimation of derivatives in nonparametric regression 2016 ·Journal of Machine Learning Research ·Wenlin Dai, Tiejun Tong, Marc G. Genton	20
13	Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models 2016 ·Journal of Multivariate Analysis ·(unknown), Gaorong Li, Heng Lian, Tiejun Tong	14
14	Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data 2015 ·Journal of Multivariate Analysis ·(unknown), Herbert Pang, Tiejun Tong, Marc G. Genton	20
15	Tail Probability Ratios of Normal and Student’s t Distributions 2014 ·Communication in Statistics- Theory and Methods ·Tiejun Tong, Heng Peng	1
16	Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices 2014 ·Journal of Multivariate Analysis ·Cheng Wang, Tiejun Tong, Longbing Cao, Miao Bai-qi	8
17	Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range breakdown → 2014 ·BMC Medical Research Methodology ·Xiang Wan, Wenqian Wang, Jiming Liu, Tiejun Tong	7836
18	Inferring Epidemic Network Topology from Surveillance Data 2014 ·PLoS ONE ·Xiang Wan, Jiming Liu, William K. Cheung, Tiejun Tong	14
19	Learning to improve medical decision making from imbalanced data without a priori cost 2014 ·BMC Medical Informatics and Decision Making ·Xiang Wan, Jiming Liu, William K. Cheung, Tiejun Tong	17
20	James–Stein type estimators of variances 2012 ·Journal of Multivariate Analysis ·Tiejun Tong, (unknown), Yuedong Wang	4

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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