Man‐Lai Tang

2.8k total citations
173 papers, 1.9k citations indexed

About

Man‐Lai Tang is a scholar working on Statistics and Probability, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, Man‐Lai Tang has authored 173 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 148 papers in Statistics and Probability, 39 papers in Management Science and Operations Research and 36 papers in Artificial Intelligence. Recurrent topics in Man‐Lai Tang's work include Statistical Methods and Bayesian Inference (82 papers), Statistical Methods and Inference (68 papers) and Statistical Methods in Clinical Trials (53 papers). Man‐Lai Tang is often cited by papers focused on Statistical Methods and Bayesian Inference (82 papers), Statistical Methods and Inference (68 papers) and Statistical Methods in Clinical Trials (53 papers). Man‐Lai Tang collaborates with scholars based in Hong Kong, China and United States. Man‐Lai Tang's co-authors include Niansheng Tang, Guo‐Liang Tian, Kai Wang Ng, Hon Keung Tony Ng, Maozai Tian, Ming Tan, Wai‐Yin Poon, Ping Shing Chan, William R. Schucany and Jiajuan Liang and has published in prestigious journals such as PLoS ONE, Biometrics and IEEE Transactions on Image Processing.

In The Last Decade

Man‐Lai Tang

159 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Man‐Lai Tang Hong Kong 21 1.3k 315 229 198 132 173 1.9k
Xavier Bry France 10 1.4k 1.1× 273 0.9× 144 0.6× 138 0.7× 106 0.8× 37 2.4k
James G. Scott United States 23 1.3k 1.0× 955 3.0× 199 0.9× 124 0.6× 74 0.6× 65 3.2k
Malay Ghosh United States 24 1.6k 1.2× 599 1.9× 467 2.0× 331 1.7× 132 1.0× 102 2.5k
Chuanhai Liu United States 21 1.2k 0.9× 792 2.5× 177 0.8× 189 1.0× 62 0.5× 65 2.0k
Siddhartha R. Dalal United States 27 321 0.2× 464 1.5× 255 1.1× 114 0.6× 140 1.1× 72 2.9k
Niansheng Tang China 21 1.2k 1.0× 455 1.4× 216 0.9× 112 0.6× 26 0.2× 177 1.8k
James P. Hobert United States 22 1.5k 1.2× 912 2.9× 196 0.9× 71 0.4× 46 0.3× 68 2.1k
Nanny Wermuth Germany 23 1.0k 0.8× 962 3.1× 228 1.0× 147 0.7× 86 0.7× 58 2.2k
A. Albert Belgium 20 496 0.4× 138 0.4× 126 0.6× 168 0.8× 64 0.5× 44 1.8k
Lawrence Brown United States 13 554 0.4× 203 0.6× 184 0.8× 130 0.7× 76 0.6× 31 1.5k

Countries citing papers authored by Man‐Lai Tang

Since Specialization
Citations

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

Fields of papers citing papers by Man‐Lai Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Man‐Lai Tang. 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 Man‐Lai Tang. The network helps show where Man‐Lai Tang may publish in the future.

Co-authorship network of co-authors of Man‐Lai Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Man‐Lai Tang. A scholar is included among the top collaborators of Man‐Lai Tang 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 Man‐Lai Tang. Man‐Lai Tang 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.
Tang, Man‐Lai, et al.. (2025). Structure-Preserving Low-Rank Model Reduction for Second-Order Time-Delay Systems. Mathematics. 13(3). 474–474.
2.
3.
Han, Linbo, et al.. (2024). Classify breast cancer pathological tissue images using multi-scale bar convolution pooling structure with patch attention. Biomedical Signal Processing and Control. 96. 106607–106607. 2 indexed citations
4.
Tian, Yuzhu, et al.. (2024). An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm. Air Quality Atmosphere & Health. 17(12). 2819–2837. 1 indexed citations
5.
Tang, Man‐Lai, et al.. (2021). Estimating treatment effects in the presence of unobserved confounders. Communications in Statistics - Simulation and Computation. 52(10). 4685–4704.
6.
Tian, Guo‐Liang, et al.. (2021). Proportional inverse Gaussian distribution: A new tool for analysing continuous proportional data. Australian & New Zealand Journal of Statistics. 63(4). 579–605.
7.
Tian, Yuzhu, et al.. (2019). Likelihood-based quantile autoregressive distributed lag models and its applications. Journal of Applied Statistics. 47(1). 117–131.
8.
Tian, Guo‐Liang, et al.. (2018). Type I multivariate zero-truncated/adjusted Poisson distributions with applications. Journal of Computational and Applied Mathematics. 344. 132–153. 9 indexed citations
9.
Tang, Niansheng, et al.. (2015). Confidence interval construction for the difference between two correlated proportions with missing observations. Journal of Biopharmaceutical Statistics. 26(2). 323–338. 7 indexed citations
10.
Tang, Niansheng, Bin Yu, & Man‐Lai Tang. (2014). Testing non-inferiority of a new treatment in three-arm clinical trials with binary endpoints. BMC Medical Research Methodology. 14(1). 134–134. 12 indexed citations
11.
Ng, Kai Wang, Guo‐Liang Tian, & Man‐Lai Tang. (2011). Dirichlet and Related Distributions. Wiley series in probability and statistics. 82 indexed citations
12.
Li, Huiqiong, Man‐Lai Tang, Wai‐Yin Poon, & Niansheng Tang. (2011). Confidence Intervals for Difference Between Two Poisson Rates. Communications in Statistics - Simulation and Computation. 40(9). 1478–1493. 17 indexed citations
13.
Tang, Man‐Lai, et al.. (2010). Correction. Statistics in Medicine. 29(20). 2168–2168. 7 indexed citations
14.
Ng, Kai Wang, et al.. (2009). THE NESTED DIRICHLET DISTRIBUTION AND INCOMPLETE CATEGORICAL DATA ANALYSIS. Statistica Sinica. 19(1). 251–271. 5 indexed citations
15.
Ng, Kai Wang, Man‐Lai Tang, Ming Tan, & Guo‐Liang Tian. (2007). Grouped Dirichlet distribution: A new tool for incomplete categorical data analysis. Journal of Multivariate Analysis. 99(3). 490–509. 16 indexed citations
16.
Tian, Guo‐Liang, et al.. (2007). A new non‐randomized model for analysing sensitive questions with binary outcomes. Statistics in Medicine. 26(23). 4238–4252. 30 indexed citations
17.
Tang, Niansheng, et al.. (2003). On tests of equivalence via non‐unity relative risk for matched‐pair design. Statistics in Medicine. 22(8). 1217–1233. 88 indexed citations
18.
Tang, Man‐Lai. (2000). On tests of linearity for dose response data: Asymptotic, exact conditional and exact unconditional tests. Journal of Applied Statistics. 27(7). 871–880. 2 indexed citations
19.
Tang, Man‐Lai, Karim F. Hirji, & Dan J. Stein. (1995). Exact power computation for dose—response studies. Statistics in Medicine. 14(20). 2261–2272. 8 indexed citations
20.
Lee, Sik‐Yum & Man‐Lai Tang. (1992). Analysis of structural equation models with Incomplete Polytomous Data. Communication in Statistics- Theory and Methods. 21(1). 213–232. 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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