Xiaogang Su

3.2k total citations · 1 hit paper
82 papers, 2.0k citations indexed

About

Xiaogang Su is a scholar working on Statistics and Probability, Pediatrics, Perinatology and Child Health and Artificial Intelligence. According to data from OpenAlex, Xiaogang Su has authored 82 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Statistics and Probability, 11 papers in Pediatrics, Perinatology and Child Health and 10 papers in Artificial Intelligence. Recurrent topics in Xiaogang Su's work include Statistical Methods and Inference (26 papers), Advanced Causal Inference Techniques (8 papers) and Statistical Methods and Bayesian Inference (8 papers). Xiaogang Su is often cited by papers focused on Statistical Methods and Inference (26 papers), Advanced Causal Inference Techniques (8 papers) and Statistical Methods and Bayesian Inference (8 papers). Xiaogang Su collaborates with scholars based in United States, China and Netherlands. Xiaogang Su's co-authors include Chih‐Ling Tsai, Xin Yan, Juanjuan Fan, Xuedong Yan, Rami Harb, Patrick McNees, Essam Radwan, Victoria Loerzel, Stephen H. Richards and Karen Meneses and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Biometrics.

In The Last Decade

Xiaogang Su

77 papers receiving 1.9k citations

Hit Papers

Linear regression 2012 2026 2016 2021 2012 100 200 300
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.

  1. Linear regression
    2012 322 citations Xiaogang Su, Chih‐Ling Tsai et al. profile →

Peers

Xiaogang Su
Alireza Daneshkhah United Kingdom
Gary Smith United States
Lili Tian United States
Stephen J. Mooney United States
Liz Cheek United Kingdom
Abbas Moghimbeigi Iran
Stephan Dreiseitl Austria
Pascal Wild France
Michael E. Miller United States
Viv Bewick United Kingdom
Alireza Daneshkhah United Kingdom
Xiaogang Su
Citations per year, relative to Xiaogang Su Xiaogang Su (= 1×) peers Alireza Daneshkhah

Countries citing papers authored by Xiaogang Su

Since Specialization
Citations

This map shows the geographic impact of Xiaogang Su'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 Xiaogang Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaogang Su more than expected).

Fields of papers citing papers by Xiaogang Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaogang Su. 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 Xiaogang Su. The network helps show where Xiaogang Su may publish in the future.

Co-authorship network of co-authors of Xiaogang Su

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaogang Su. A scholar is included among the top collaborators of Xiaogang Su 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 Xiaogang Su. Xiaogang Su is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lee, Wen‐Yee, et al.. (2024). Urinary volatile organic compounds (VOCs) based prostate cancer diagnosis via high-dimensional classification. Journal of Applied Statistics. 51(16). 3468–3485.
2.
Miller, J. Philip, et al.. (2024). Precision Medicine: Interaction Survival Tree for Recurrent Event Data. Journal of Data Science. 298–313.
3.
Su, Xiaogang, et al.. (2023). Refined moderation analysis with binary outcomes in precision medicine research. Statistical Methods in Medical Research. 32(4). 732–747. 1 indexed citations
4.
Yang, Feng, et al.. (2023). Vibration training reducing falls in community-living older adults: a pilot randomized controlled trial. Aging Clinical and Experimental Research. 35(4). 803–814. 4 indexed citations
5.
Su, Xiaogang, et al.. (2022). Refined moderation analysis with categorical outcomes in precision medicine. Statistics in Medicine. 42(4). 470–486. 2 indexed citations
6.
Liu, Lei, et al.. (2020). Precision medicine: Subgroup identification in longitudinal trajectories. Statistical Methods in Medical Research. 29(9). 2603–2616. 12 indexed citations
7.
Su, Xiaogang, et al.. (2020). Variable selection in joint frailty models of recurrent and terminal events. Biometrics. 76(4). 1330–1339. 6 indexed citations
8.
Wang, Wenjing, Bin Su, Lijun Pang, et al.. (2020). High-dimensional immune profiling by mass cytometry revealed immunosuppression and dysfunction of immunity in COVID-19 patients. Cellular and Molecular Immunology. 17(6). 650–652. 99 indexed citations
9.
Dusseldorp, Elise, et al.. (2020). Multiple moderator meta-analysis using the R-package Meta-CART. Behavior Research Methods. 52(6). 2657–2673. 37 indexed citations
10.
Su, Xiaogang, et al.. (2018). Random forests of interaction trees for estimating individualized treatment effects in randomized trials. Statistics in Medicine. 37(17). 2547–2560. 26 indexed citations
11.
Liu, Lei, et al.. (2018). Variable selection for random effects two-part models. Statistical Methods in Medical Research. 28(9). 2697–2709. 4 indexed citations
12.
Yang, Feng, et al.. (2018). Relative importance of physical and psychological factors to slowness in people with mild to moderate multiple sclerosis. Multiple Sclerosis and Related Disorders. 27. 81–90. 17 indexed citations
13.
Holditch‐Davis, Diane, et al.. (2017). Associations Between Hormonal Biomarkers and Cognitive, Motor, and Language Developmental Status in Very Low Birth Weight Infants. Nursing Research. 66(5). 350–358. 4 indexed citations
14.
15.
Yang, Feng, et al.. (2015). Controlled whole-body vibration training reduces risk of falls among community-dwelling older adults. Journal of Biomechanics. 48(12). 3206–3212. 51 indexed citations
16.
Sharpsten, Lucie, Juanjuan Fan, Shaban Demirel, et al.. (2014). Predicting progressive glaucomatous optic neuropathy using random forests based on longitudinally collected standard automated perimetry data. Investigative Ophthalmology & Visual Science. 55(13). 5619–5619. 1 indexed citations
17.
Harb, Rami, et al.. (2008). Crash Avoidance Analysis Using Classification Trees and Random Forests. Transportation Research Board 87th Annual MeetingTransportation Research Board. 3 indexed citations
18.
Su, Xiaogang, Tianni Zhou, Xin Yan, Juanjuan Fan, & Yang Song. (2008). Interaction Trees with Censored Survival Data. The International Journal of Biostatistics. 4(1). Article 2–Article 2. 71 indexed citations
19.
Su, Xiaogang. (2008). Data Mining Methods and Models, Daniel T. Larose. The American Statistician. 62. 91–91. 1 indexed citations
20.
Su, Xiaogang & Chih‐Ling Tsai. (2006). An Improved Akaike Information Criterion for Generalized Log-Gamma Regression Models. The International Journal of Biostatistics. 2(1). 1 indexed citations

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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