Wan‐Lun Wang

1.2k total citations
72 papers, 890 citations indexed

About

Wan‐Lun Wang is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Wan‐Lun Wang has authored 72 papers receiving a total of 890 indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Statistics and Probability, 45 papers in Artificial Intelligence and 18 papers in Molecular Biology. Recurrent topics in Wan‐Lun Wang's work include Bayesian Methods and Mixture Models (45 papers), Statistical Methods and Bayesian Inference (33 papers) and Statistical Methods and Inference (26 papers). Wan‐Lun Wang is often cited by papers focused on Bayesian Methods and Mixture Models (45 papers), Statistical Methods and Bayesian Inference (33 papers) and Statistical Methods and Inference (26 papers). Wan‐Lun Wang collaborates with scholars based in Taiwan, Chile and United States. Wan‐Lun Wang's co-authors include Tsung‐I Lin, Tsai‐Hung Fan, Víctor H. Lachos, Ling‐Ming Tseng, Tzu‐Ting Huang, Pei‐Yi Chu, Chun‐Teng Huang, Chun‐Yu Liu, Luis M. Castro and Chia‐Han Lee and has published in prestigious journals such as Cancer Research, Statistics in Medicine and Experimental Cell Research.

In The Last Decade

Wan‐Lun Wang

65 papers receiving 877 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wan‐Lun Wang Taiwan 20 518 398 205 106 75 72 890
Alessandra Guglielmi Italy 14 231 0.4× 283 0.7× 95 0.5× 164 1.5× 17 0.2× 70 610
E. Olusegun George United States 15 223 0.4× 118 0.3× 196 1.0× 53 0.5× 43 0.6× 54 749
Zhenming Shun United States 9 258 0.5× 76 0.2× 175 0.9× 87 0.8× 49 0.7× 21 671
Anup Dewanji India 15 294 0.6× 64 0.2× 87 0.4× 74 0.7× 80 1.1× 63 817
Yuan‐chin Ivan Chang Taiwan 15 124 0.2× 137 0.3× 108 0.5× 17 0.2× 47 0.6× 46 517
Lianming Wang United States 15 473 0.9× 163 0.4× 60 0.3× 45 0.4× 25 0.3× 32 637
Beatrix Jones New Zealand 11 175 0.3× 207 0.5× 511 2.5× 53 0.5× 10 0.1× 16 997
Hokeun Sun South Korea 16 275 0.5× 47 0.1× 268 1.3× 33 0.3× 171 2.3× 37 751
Jürgen Läuter Germany 14 254 0.5× 56 0.1× 325 1.6× 154 1.5× 15 0.2× 42 890

Countries citing papers authored by Wan‐Lun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Wan‐Lun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wan‐Lun Wang. 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 Wan‐Lun Wang. The network helps show where Wan‐Lun Wang may publish in the future.

Co-authorship network of co-authors of Wan‐Lun Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Wan‐Lun Wang. A scholar is included among the top collaborators of Wan‐Lun Wang 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 Wan‐Lun Wang. Wan‐Lun Wang 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.
Lin, Tsung‐I & Wan‐Lun Wang. (2025). Finite Mixtures of Multivariate Contaminated Normal Censored Regression Models. Journal of Computational and Graphical Statistics. 35(1). 13–26.
2.
Wang, Wan‐Lun, et al.. (2025). Mixtures of common factor analyzers using the restricted multivariate skew-t distribution for clustering high-dimensional data with missing values. Journal of Computational and Applied Mathematics. 471. 116708–116708.
3.
Wang, Wan‐Lun, Víctor H. Lachos, Yuanzhi Chen, & Tsung‐I Lin. (2025). Flexible clustering via Gaussian parsimonious mixture models with censored and missing values. Test. 34(2). 431–458. 1 indexed citations
4.
Lin, Tsung‐I & Wan‐Lun Wang. (2025). Multivariate contaminated normal linear mixed models applied to Alzheimer’s disease study with censored and missing data. Statistical Methods in Medical Research. 34(3). 490–507. 2 indexed citations
5.
Wang, Wan‐Lun, et al.. (2024). Three-way data clustering based on the mean-mixture of matrix-variate normal distributions. Computational Statistics & Data Analysis. 199. 108016–108016. 1 indexed citations
6.
Lin, Tsung‐I, et al.. (2024). Robust Bayesian inference for the censored mixture of experts model using heavy-tailed distributions. Advances in Data Analysis and Classification. 19(4). 921–949.
7.
Wang, Wan‐Lun. (2023). Multivariate Contaminated Normal Censored Regression Model: Properties and Maximum Likelihood Inference. Journal of Computational and Graphical Statistics. 32(4). 1671–1684. 2 indexed citations
8.
Tseng, Ling‐Ming, Ji‐Lin Chen, Pei‐Yi Chu, et al.. (2023). Regorafenib induces damage-associated molecular patterns, cancer cell death and immune modulatory effects in a murine triple negative breast cancer model. Experimental Cell Research. 429(1). 113652–113652. 7 indexed citations
9.
Lin, Tsung‐I, et al.. (2021). On moments of folded and truncated multivariate Student-t distributions based on recurrence relations. Metrika. 84(6). 825–850. 9 indexed citations
10.
Liu, Chun‐Yu, Ji‐Lin Chen, Chun‐Teng Huang, et al.. (2021). Abstract 2486: Pleiotropic anti-cancer effects of STING in estrogen receptor positive breast cancer cells. Cancer Research. 81(13_Supplement). 2486–2486. 1 indexed citations
11.
Huang, Tzu‐Ting, Ling‐Ming Tseng, Ji‐Lin Chen, et al.. (2020). Kynurenine 3-monooxygenase upregulates pluripotent genes through β-catenin and promotes triple-negative breast cancer progression. EBioMedicine. 54. 102717–102717. 34 indexed citations
12.
Wang, Wan‐Lun, Luis M. Castro, & Tsung‐I Lin. (2017). Automated learning oftfactor analysis models with complete and incomplete data. Journal of Multivariate Analysis. 161. 157–171. 7 indexed citations
13.
Liu, Chun‐Yu, Kuen‐Feng Chen, Pei‐Yi Chu, et al.. (2017). Sequential combination of docetaxel with a SHP-1 agonist enhanced suppression of p-STAT3 signaling and apoptosis in triple negative breast cancer cells. Journal of Molecular Medicine. 95(9). 965–975. 22 indexed citations
14.
Chen, Po‐Ming, Pei‐Yi Chu, Shiao-Lin Tung, et al.. (2017). Overexpression of phosphoprotein phosphatase 2A predicts worse prognosis in patients with breast cancer: a 15-year follow-up. Human Pathology. 66. 93–100. 8 indexed citations
15.
16.
Liu, Chun‐Yu, Tzu‐Ting Huang, Pei‐Yi Chu, et al.. (2017). Sorafenib analogue SC‐60 induces apoptosis through the SHP‐1/STAT3 pathway and enhances docetaxel cytotoxicity in triple‐negative breast cancer cells. Molecular Oncology. 11(3). 266–279. 30 indexed citations
17.
Liu, Chun‐Yu, Tzu‐Ting Huang, Chun‐Teng Huang, et al.. (2016). EGFR-independent Elk1/CIP2A signalling mediates apoptotic effect of an erlotinib derivative TD52 in triple-negative breast cancer cells. European Journal of Cancer. 72. 112–123. 37 indexed citations
18.
Wang, Wan‐Lun & Tsung‐I Lin. (2016). Maximum likelihood inference for the multivariate t mixture model. Journal of Multivariate Analysis. 149. 54–64. 13 indexed citations
19.
Wang, Wan‐Lun. (2013). Mixtures of common factor analyzers for high-dimensional data with missing information. Journal of Multivariate Analysis. 117. 120–133. 14 indexed citations
20.
Wang, Wan‐Lun & Tsai‐Hung Fan. (2011). Bayesian analysis of multivariate t linear mixed models using a combination of IBF and Gibbs samplers. Journal of Multivariate Analysis. 105(1). 300–310. 23 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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