Yuan‐chin Ivan Chang

733 total citations
46 papers, 517 citations indexed

About

Yuan‐chin Ivan Chang is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Yuan‐chin Ivan Chang has authored 46 papers receiving a total of 517 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Statistics and Probability, 18 papers in Artificial Intelligence and 12 papers in Management Science and Operations Research. Recurrent topics in Yuan‐chin Ivan Chang's work include Statistical Methods and Inference (13 papers), Imbalanced Data Classification Techniques (9 papers) and Advanced Statistical Methods and Models (8 papers). Yuan‐chin Ivan Chang is often cited by papers focused on Statistical Methods and Inference (13 papers), Imbalanced Data Classification Techniques (9 papers) and Advanced Statistical Methods and Models (8 papers). Yuan‐chin Ivan Chang collaborates with scholars based in Taiwan, South Korea and China. Yuan‐chin Ivan Chang's co-authors include Zhanfeng Wang, Chii‐Ruey Tzeng, Wei‐Ning Yang, Ming-I Hsu, Jim Jinn‐Chyuan Sheu, Adam T. Martinsek, Zhiliang Ying, Chia‐Jung Li, An‐Jen Chiang and Chung Chang and has published in prestigious journals such as Bioinformatics, PLoS ONE and Biometrics.

In The Last Decade

Yuan‐chin Ivan Chang

40 papers receiving 488 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yuan‐chin Ivan Chang Taiwan 15 137 124 108 64 51 46 517
Diane E. Duffy United States 10 133 1.0× 344 2.8× 70 0.6× 97 1.5× 3 0.1× 15 725
Subrata Chakraborty India 16 177 1.3× 642 5.2× 69 0.6× 110 1.7× 31 0.6× 107 1.0k
Baoxue Zhang China 15 193 1.4× 163 1.3× 129 1.2× 18 0.3× 2 0.0× 66 754
Stijn Meganck Belgium 10 317 2.3× 26 0.2× 596 5.5× 34 0.5× 11 0.2× 25 1.1k
Martin Slawski United States 14 353 2.6× 65 0.5× 220 2.0× 23 0.4× 3 0.1× 35 916
Arthur Tenenhaus France 16 72 0.5× 50 0.4× 321 3.0× 26 0.4× 3 0.1× 26 988
Jacek Koronacki Poland 12 172 1.3× 52 0.4× 178 1.6× 21 0.3× 3 0.1× 30 523
Jooyong Shim South Korea 10 77 0.6× 113 0.9× 85 0.8× 54 0.8× 2 0.0× 63 382
Marcel Dettling Switzerland 11 349 2.5× 162 1.3× 772 7.1× 19 0.3× 5 0.1× 19 1.2k

Countries citing papers authored by Yuan‐chin Ivan Chang

Since Specialization
Citations

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

Fields of papers citing papers by Yuan‐chin Ivan Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuan‐chin Ivan Chang. 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 Yuan‐chin Ivan Chang. The network helps show where Yuan‐chin Ivan Chang may publish in the future.

Co-authorship network of co-authors of Yuan‐chin Ivan Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Yuan‐chin Ivan Chang. A scholar is included among the top collaborators of Yuan‐chin Ivan Chang 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 Yuan‐chin Ivan Chang. Yuan‐chin Ivan Chang 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.
Chang, Yuan‐chin Ivan, et al.. (2023). Distributed sequential estimation procedures. Canadian Journal of Statistics. 52(1). 271–290. 1 indexed citations
2.
Tripathi, Yogesh Mani, et al.. (2022). A nonlinear correlation measure with applications to gene expression data. PLoS ONE. 17(6). e0270270–e0270270. 1 indexed citations
3.
Li, Chia‐Jung, Yi‐Han Chiu, Chung Chang, et al.. (2021). Acetyl Coenzyme A Synthase 2 Acts as a Prognostic Biomarker Associated with Immune Infiltration in Cervical Squamous Cell Carcinoma. Cancers. 13(13). 3125–3125. 32 indexed citations
4.
Chiang, An‐Jen, Chia‐Jung Li, Kuan‐Hao Tsui, et al.. (2020). UBE2C Drives Human Cervical Cancer Progression and Is Positively Modulated by mTOR. Biomolecules. 11(1). 37–37. 37 indexed citations
5.
Chang, Yuan‐chin Ivan, et al.. (2019). Sequential adaptive variables and subject selection for GEE methods. Biometrics. 76(2). 496–507. 4 indexed citations
6.
Chang, Yuan‐chin Ivan, et al.. (2018). Greedy active learning algorithm for logistic regression models. Computational Statistics & Data Analysis. 129. 119–134. 3 indexed citations
7.
Chang, Yuan‐chin Ivan & Ray‐Bing Chen. (2018). Active learning with simultaneous subject and variable selections. Neurocomputing. 329. 495–505. 2 indexed citations
8.
Liou, Tsan‐Hon, et al.. (2015). Obesity and inflammatory biomarkers in women with polycystic ovary syndrome. European Journal of Obstetrics & Gynecology and Reproductive Biology. 192. 66–71. 29 indexed citations
9.
Tzeng, Chii‐Ruey, Yuan‐chin Ivan Chang, Yu-Chia Chang, et al.. (2014). Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertility and Sterility. 101(5). 1404–1410.e1. 33 indexed citations
10.
Chang, Yuan‐chin Ivan, et al.. (2014). Biomarker selection for medical diagnosis using the partial area under the ROC curve. BMC Research Notes. 7(1). 25–25. 25 indexed citations
11.
Yu, Wenbao, Eunsik Park, & Yuan‐chin Ivan Chang. (2014). Comparison of Paired ROC Curves through a Two-Stage Test. Journal of Biopharmaceutical Statistics. 25(5). 881–902. 5 indexed citations
12.
Lin, Yi-Hui, Shih‐Yi Huang, Ming-I Hsu, et al.. (2013). Hyperhomocysteinaemia is associated with biochemical hyperandrogenaemia in women with reproductive age. European Journal of Obstetrics & Gynecology and Reproductive Biology. 171(2). 314–318. 11 indexed citations
13.
Yu, Wenbao, Yuan‐chin Ivan Chang, & Eunsik Park. (2013). A modified area under the ROC curve and its application to marker selection and classification. Journal of the Korean Statistical Society. 43(2). 161–175. 16 indexed citations
14.
Chang, Yuan‐chin Ivan, et al.. (2011). Optimal sampling in retrospective logistic regression via two-stage method. Biometrical Journal. 53(1). 5–18. 1 indexed citations
15.
Chang, Yuan‐chin Ivan, et al.. (2010). Early stopping in L2Boosting. Computational Statistics & Data Analysis. 54(10). 2203–2213. 14 indexed citations
16.
Wang, Zhanfeng & Yuan‐chin Ivan Chang. (2010). Marker selection via maximizing the partial area under the ROC curve of linear risk scores. Biostatistics. 12(2). 369–385. 42 indexed citations
17.
Chang, Yuan‐chin Ivan & Eunsik Park. (2009). Constructing the best linear combination of diagnostic markers via sequential sampling. Statistics & Probability Letters. 79(18). 1921–1927.
18.
Chang, Yuan‐chin Ivan, et al.. (2007). A stochastic approximation view of boosting. Computational Statistics & Data Analysis. 52(1). 325–334. 13 indexed citations
19.
Young, John F., Weida Tong, Hong Fang, et al.. (2004). BUILDING AN ORGAN-SPECIFIC CARCINOGENIC DATABASE FOR SAR ANALYSES. Journal of Toxicology and Environmental Health. 67(17). 1363–1389. 13 indexed citations
20.
Chang, Yuan‐chin Ivan. (1996). SEQUENTIAL FIXED SIZE CONFIDENCE REGIONS FOR REGRESSION PARAMETERS IN GENERALIZED LINEAR MODELS. 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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