Chin‐Tsang Chiang

1.4k total citations
32 papers, 999 citations indexed

About

Chin‐Tsang Chiang is a scholar working on Statistics and Probability, Artificial Intelligence and Economics and Econometrics. According to data from OpenAlex, Chin‐Tsang Chiang has authored 32 papers receiving a total of 999 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Statistics and Probability, 12 papers in Artificial Intelligence and 3 papers in Economics and Econometrics. Recurrent topics in Chin‐Tsang Chiang's work include Statistical Methods and Inference (26 papers), Statistical Methods and Bayesian Inference (19 papers) and Bayesian Methods and Mixture Models (9 papers). Chin‐Tsang Chiang is often cited by papers focused on Statistical Methods and Inference (26 papers), Statistical Methods and Bayesian Inference (19 papers) and Bayesian Methods and Mixture Models (9 papers). Chin‐Tsang Chiang collaborates with scholars based in Taiwan, United States and Hong Kong. Chin‐Tsang Chiang's co-authors include Colin O. Wu, Donald R. Hoover, Mei‐Cheng Wang, Hung Hung, Jing Qin, John A. Rice, Kenzie L. Preston, Iván D. Montoya, Annie Umbricht and Kuang‐Yao Lee and has published in prestigious journals such as Journal of the American Statistical Association, Biometrics and Statistics in Medicine.

In The Last Decade

Chin‐Tsang Chiang

31 papers receiving 966 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chin‐Tsang Chiang Taiwan 12 706 189 99 70 56 32 999
D. Y. Lin United States 13 755 1.1× 127 0.7× 105 1.1× 52 0.7× 72 1.3× 22 1.1k
David B. Wolfson Canada 17 732 1.0× 170 0.9× 174 1.8× 75 1.1× 107 1.9× 54 1.4k
Grace Y. Yi Canada 18 673 1.0× 163 0.9× 122 1.2× 23 0.3× 44 0.8× 66 883
Albert Vexler United States 19 749 1.1× 118 0.6× 47 0.5× 79 1.1× 71 1.3× 95 1.4k
Lu Lin China 13 243 0.3× 91 0.5× 80 0.8× 36 0.5× 41 0.7× 51 979
Donglin Zeng United States 20 910 1.3× 233 1.2× 105 1.1× 68 1.0× 83 1.5× 56 1.6k
Riquan Zhang China 15 632 0.9× 183 1.0× 114 1.2× 18 0.3× 46 0.8× 136 950
Liuquan Sun China 18 992 1.4× 238 1.3× 134 1.4× 12 0.2× 66 1.2× 132 1.1k
Lurdes Y. T. Inoue United States 22 309 0.4× 85 0.4× 129 1.3× 37 0.5× 133 2.4× 48 1.4k
Mojtaba Ganjali Iran 14 537 0.8× 157 0.8× 69 0.7× 45 0.6× 54 1.0× 121 898

Countries citing papers authored by Chin‐Tsang Chiang

Since Specialization
Citations

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

Fields of papers citing papers by Chin‐Tsang Chiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chin‐Tsang Chiang. 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 Chin‐Tsang Chiang. The network helps show where Chin‐Tsang Chiang may publish in the future.

Co-authorship network of co-authors of Chin‐Tsang Chiang

This figure shows the co-authorship network connecting the top 25 collaborators of Chin‐Tsang Chiang. A scholar is included among the top collaborators of Chin‐Tsang Chiang 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 Chin‐Tsang Chiang. Chin‐Tsang Chiang 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.
Chiang, Chin‐Tsang, et al.. (2023). An effective method for identifying clusters of robot strengths. Computational Statistics. 39(6). 3303–3345. 1 indexed citations
2.
Chiang, Chin‐Tsang, et al.. (2021). Estimating robot strengths with application to selection of alliance members in FIRST robotics competitions. Computational Statistics & Data Analysis. 158. 107181–107181. 1 indexed citations
3.
Chiang, Chin‐Tsang, et al.. (2016). An Effective Semiparametric Estimation Approach for the Sufficient Dimension Reduction Model. Journal of the American Statistical Association. 112(519). 1296–1310. 9 indexed citations
4.
Chiang, Chin‐Tsang, et al.. (2016). ROC representation for the discriminability of multi-classification markers. Pattern Recognition. 60. 770–777. 4 indexed citations
5.
Chiang, Chin‐Tsang, et al.. (2013). Binary response models withM-phase case-control data. Journal of Multivariate Analysis. 116. 332–348.
6.
Chiang, Chin‐Tsang, et al.. (2012). New estimation and inference procedures for a single-index conditional distribution model. Journal of Multivariate Analysis. 111. 271–285. 8 indexed citations
7.
Chiang, Chin‐Tsang, et al.. (2011). Nonparametric and semiparametric optimal transformations of markers. Journal of Multivariate Analysis. 103(1). 124–141. 1 indexed citations
8.
Hung, Hung & Chin‐Tsang Chiang. (2011). Nonparametric methodology for the time-dependent partial area under the ROC curve. Journal of Statistical Planning and Inference. 141(12). 3829–3838. 4 indexed citations
9.
Chiang, Chin‐Tsang, et al.. (2009). RANDOM WEIGHTING AND EDGEWORTH EXPANSION FOR THE NONPARAMETRIC TIME-DEPENDENT AUC ESTIMATOR. Statistica Sinica. 19(3). 969–979. 10 indexed citations
10.
Hung, Hung & Chin‐Tsang Chiang. (2009). Estimation methods for time‐dependent AUC models with survival data. Canadian Journal of Statistics. 38(1). 8–26. 106 indexed citations
11.
Chiang, Chin‐Tsang & Kuang‐Yao Lee. (2008). EFFICIENT ESTIMATION METHODS FOR INFORMATIVE CLUSTER SIZE DATA. 11 indexed citations
12.
Chiang, Chin‐Tsang, et al.. (2008). Estimation for the Optimal Combination of Markers without Modeling the Censoring Distribution. Biometrics. 65(1). 152–158. 3 indexed citations
13.
Chiang, Chin‐Tsang & Mei‐Cheng Wang. (2007). Varying-coefficient model for the occurrence rate function of recurrent events. Annals of the Institute of Statistical Mathematics. 61(1). 197–213. 7 indexed citations
14.
Chiang, Chin‐Tsang, Lancelot F. James, & Mei‐Cheng Wang. (2005). Random Weighted Bootstrap Method for Recurrent Events with Informative Censoring. Lifetime Data Analysis. 11(4). 489–509. 11 indexed citations
15.
Chiang, Chin‐Tsang, Mei‐Cheng Wang, & Chiung‐Yu Huang. (2005). Kernel Estimation of Rate Function for Recurrent Event Data. Scandinavian Journal of Statistics. 32(1). 77–91. 14 indexed citations
16.
Chiang, Chin‐Tsang, et al.. (2004). Smoothing estimation of rate function for recurrent event data with informative censoring. Annals of the Institute of Statistical Mathematics. 56(1). 87–100. 2 indexed citations
17.
Wang, Mei‐Cheng & Chin‐Tsang Chiang. (2002). Non‐parametric methods for recurrent event data with informative and non‐informative censorings. Statistics in Medicine. 21(3). 445–456. 9 indexed citations
18.
Wang, Mei‐Cheng, Jing Qin, & Chin‐Tsang Chiang. (2001). Analyzing Recurrent Event Data With Informative Censoring. Journal of the American Statistical Association. 96(455). 1057–1065. 196 indexed citations
19.
Umbricht, Annie, et al.. (1999). Naltrexone shortened opioid detoxification with buprenorphine. Drug and Alcohol Dependence. 56(3). 181–190. 65 indexed citations
20.
Wu, Colin O., Chin‐Tsang Chiang, & Donald R. Hoover. (1998). Asymptotic Confidence Regions for Kernel Smoothing of a Varying-Coefficient Model with Longitudinal Data. Journal of the American Statistical Association. 93(444). 1388–1402. 194 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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2026