Tsung‐I Lin

3.4k total citations
92 papers, 2.3k citations indexed

About

Tsung‐I Lin is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Tsung‐I Lin has authored 92 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Statistics and Probability, 69 papers in Artificial Intelligence and 10 papers in Molecular Biology. Recurrent topics in Tsung‐I Lin's work include Bayesian Methods and Mixture Models (69 papers), Statistical Methods and Bayesian Inference (45 papers) and Statistical Distribution Estimation and Applications (45 papers). Tsung‐I Lin is often cited by papers focused on Bayesian Methods and Mixture Models (69 papers), Statistical Methods and Bayesian Inference (45 papers) and Statistical Distribution Estimation and Applications (45 papers). Tsung‐I Lin collaborates with scholars based in Taiwan, United States and Iran. Tsung‐I Lin's co-authors include Jack C. Lee, Wan‐Lun Wang, Geoffrey J. McLachlan, Saumyadipta Pyne, Jack C. Lee, Elizabeth J. Rossin, Jill P. Mesirov, Philip L. De Jager, Lisa M. Maier and Clare Baecher‐Allan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Bioinformatics and Journal of Hepatology.

In The Last Decade

Tsung‐I Lin

83 papers receiving 2.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tsung‐I Lin Taiwan	24	1.5k	1.4k	383	241	132	92	2.3k
Tobias Rydèn Sweden	20	413 0.3×	654 0.5×	261 0.7×	185 0.8×	39 0.3×	55	1.9k
Subhashis Ghosal United States	25	1.7k 1.2×	1.5k 1.1×	97 0.3×	181 0.8×	166 1.3×	93	2.5k
Chenlei Leng Singapore	22	1.4k 0.9×	498 0.4×	194 0.5×	129 0.5×	102 0.8×	75	2.1k
Kam‐Wah Tsui United States	16	775 0.5×	261 0.2×	625 1.6×	98 0.4×	205 1.6×	55	1.6k
John T. Ormerod Australia	17	487 0.3×	454 0.3×	259 0.7×	37 0.2×	30 0.2×	50	1.1k
Sanat K. Sarkar United States	15	1.6k 1.1×	407 0.3×	217 0.6×	404 1.7×	317 2.4×	65	2.4k
Bing‐Yi Jing Hong Kong	27	1.2k 0.8×	458 0.3×	110 0.3×	657 2.7×	138 1.0×	102	2.3k
Carlos M. Carvalho United States	13	471 0.3×	402 0.3×	291 0.8×	149 0.6×	20 0.2×	41	1.2k
Athanasios Kottas United States	20	764 0.5×	808 0.6×	55 0.1×	88 0.4×	72 0.5×	58	1.4k
X. Sheldon Lin Canada	27	690 0.5×	665 0.5×	315 0.8×	628 2.6×	31 0.2×	100	2.7k

Countries citing papers authored by Tsung‐I Lin

Since Specialization

Citations

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

Fields of papers citing papers by Tsung‐I Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tsung‐I Lin. 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 Tsung‐I Lin. The network helps show where Tsung‐I Lin may publish in the future.

Co-authorship network of co-authors of Tsung‐I Lin

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

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.

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.

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

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

Hu, Ya‐Han, et al.. (2024). A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications. BMC Medical Research Methodology. 24(1). 269–269. 3 indexed citations

Mahdavi, Abbas, et al.. (2021). A Multivariate Flexible Skew-Symmetric-Normal Distribution: Scale-Shape Mixtures and Parameter Estimation via Selection Representation. Symmetry. 13(8). 1343–1343. 8 indexed citations

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

Garay, Aldo M., et al.. (2020). Bayesian analysis of the p-order integer-valued AR process with zero-inflated Poisson innovations. Journal of Statistical Computation and Simulation. 90(11). 1943–1964. 2 indexed citations

Lee, Sharon, Tsung‐I Lin, & Geoffrey J. McLachlan. (2020). Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. Advances in Data Analysis and Classification. 15(2). 481–512. 7 indexed citations

10.

Lachos, Víctor H., et al.. (2018). Heavy-tailed longitudinal regression models for censored data: a robust parametric approach. Test. 28(3). 844–878. 8 indexed citations

11.

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

12.

Lin, Tsung‐I, Geoffrey J. McLachlan, & Sharon Lee. (2015). Extending mixtures of factor models using the restricted multivariate skew-normal distribution. Journal of Multivariate Analysis. 143. 398–413. 45 indexed citations

13.

Liu, Michael C. & Tsung‐I Lin. (2014). Skew-normal factor analysis models with incomplete data. Journal of Applied Statistics. 42(4). 789–805. 13 indexed citations

14.

Ho, Hsiu J., Tsung‐I Lin, Hannah Chang, et al.. (2012). Parametric modeling of cellular state transitions as measured with flow cytometry. BMC Bioinformatics. 13(S5). S5–S5. 9 indexed citations

15.

Lin, Tsung‐I, et al.. (2009). Analysis of multivariate skew normal models with incomplete data. Journal of Multivariate Analysis. 100(10). 2337–2351. 32 indexed citations

16.

Lin, Tsung‐I. (2008). Maximum likelihood estimation for multivariate skew normal mixture models. Journal of Multivariate Analysis. 100(2). 257–265. 127 indexed citations

17.

Lin, Tsung‐I, et al.. (2007). Finite mixture modelling using the skew normal distribution. Statistica Sinica. 17(3). 909–927. 147 indexed citations

18.

Lin, Tsung‐I & Jack C. Lee. (2007). Estimation and prediction in linear mixed models with skew‐normal random effects for longitudinal data. Statistics in Medicine. 27(9). 1490–1507. 71 indexed citations

19.

Lin, Tsung‐I & Jack C. Lee. (2006). Bayesian analysis of hierarchical linear mixed modeling using the multivariate t distribution. Journal of Statistical Planning and Inference. 137(2). 484–495. 40 indexed citations

20.

Lin, Tsung‐I & Jack C. Lee. (2005). A robust approach tot linear mixed models applied to multiple sclerosis data. Statistics in Medicine. 25(8). 1397–1412. 34 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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