Liming Xiang

2.2k total citations
86 papers, 1.6k citations indexed

About

Liming Xiang is a scholar working on Statistics and Probability, Artificial Intelligence and Mechanical Engineering. According to data from OpenAlex, Liming Xiang has authored 86 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Statistics and Probability, 23 papers in Artificial Intelligence and 19 papers in Mechanical Engineering. Recurrent topics in Liming Xiang's work include Statistical Methods and Inference (28 papers), Statistical Methods and Bayesian Inference (26 papers) and Bayesian Methods and Mixture Models (18 papers). Liming Xiang is often cited by papers focused on Statistical Methods and Inference (28 papers), Statistical Methods and Bayesian Inference (26 papers) and Bayesian Methods and Mixture Models (18 papers). Liming Xiang collaborates with scholars based in Singapore, China and Hong Kong. Liming Xiang's co-authors include Barış Burak Kanbur, Fei Duan, Swapnil Dubey, Fook Hoong Choo, Andy H. Lee, Xinghu Li, Lingjun Song, Kelvin K.W. Yau, Fugee Tsung and Hongming Xu and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, PLoS ONE and Journal of Power Sources.

In The Last Decade

Liming Xiang

81 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Liming Xiang Singapore 20 383 331 231 222 178 86 1.6k
M. A. Hamdan Jordan 25 729 1.9× 321 1.0× 241 1.0× 175 0.8× 296 1.7× 198 2.4k
Fuad A. Awwad Saudi Arabia 19 323 0.8× 204 0.6× 68 0.3× 35 0.2× 41 0.2× 177 1.2k
Yunlong Zhang China 30 77 0.2× 36 0.1× 262 1.1× 468 2.1× 152 0.9× 147 2.9k
Santu Rana Australia 20 184 0.5× 27 0.1× 107 0.5× 135 0.6× 549 3.1× 85 2.1k
Kaibo Liu United States 24 238 0.6× 167 0.5× 101 0.4× 142 0.6× 351 2.0× 74 2.1k
Mahdi Mahfouf United Kingdom 25 596 1.6× 90 0.3× 140 0.6× 101 0.5× 604 3.4× 217 2.4k
Yue He China 20 269 0.7× 14 0.0× 177 0.8× 51 0.2× 267 1.5× 71 1.2k
Guohui Zhang United States 38 252 0.7× 31 0.1× 346 1.5× 821 3.7× 205 1.2× 217 4.6k
Jiansong Wu China 27 132 0.3× 114 0.3× 88 0.4× 13 0.1× 80 0.4× 96 1.7k
Steven E. Rigdon United States 27 130 0.3× 1.5k 4.6× 59 0.3× 55 0.2× 151 0.8× 107 3.5k

Countries citing papers authored by Liming Xiang

Since Specialization
Citations

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

Fields of papers citing papers by Liming Xiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liming Xiang. 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 Liming Xiang. The network helps show where Liming Xiang may publish in the future.

Co-authorship network of co-authors of Liming Xiang

This figure shows the co-authorship network connecting the top 25 collaborators of Liming Xiang. A scholar is included among the top collaborators of Liming Xiang 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 Liming Xiang. Liming Xiang 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.
Ma, Yuqing, et al.. (2025). Flexible modeling of left-truncated and interval-censored competing risks data with missing event types. Computational Statistics & Data Analysis. 211. 108229–108229.
2.
Xiang, Liming, et al.. (2025). Bayesian analysis of doubly semiparametric mixture cure models with interval-censored data. Statistics and Computing. 35(3).
3.
Ma, Yuqing, et al.. (2025). A multiple imputation approach for flexible modelling of interval-censored data with missing and censored covariates. Computational Statistics & Data Analysis. 209. 108177–108177.
4.
5.
Zhao, Jian, Yun Zhao, Liming Xiang, et al.. (2019). A two-part mixed-effects model for analyzing clustered time-to-event data with clumping at zero. Computer Methods and Programs in Biomedicine. 187. 105196–105196. 4 indexed citations
6.
Ha, Il Do, et al.. (2019). Frailty modelling approaches for semi-competing risks data. Lifetime Data Analysis. 26(1). 109–133. 10 indexed citations
7.
Chua, Karen Sui Geok, et al.. (2018). Clinical and kinematic evaluation of the H-Man arm robot for post-stroke upper limb rehabilitation: Preliminary findings of a randomised controlled trial. Annals of Physical and Rehabilitation Medicine. 61. e95–e95. 6 indexed citations
8.
Hussain, Amir, Christopher Wee Keong Kuah, Liming Xiang, et al.. (2017). Proprioceptive assessment in clinical settings: Evaluation of joint position sense in upper limb post-stroke using a robotic manipulator. PLoS ONE. 12(11). e0183257–e0183257. 27 indexed citations
9.
Posadzki, Paul, Nikolaos Mastellos, Rebecca Ryan, et al.. (2016). Automated telephone communication systems for preventive healthcare and management of long-term conditions. Cochrane Database of Systematic Reviews. 2016(12). CD009921–CD009921. 90 indexed citations
10.
Hussain, Amir, Charmayne Hughes, Christopher Wee Keong Kuah, et al.. (2016). Self-Paced Reaching after Stroke: A Quantitative Assessment of Longitudinal and Directional Sensitivity Using the H-Man Planar Robot for Upper Limb Neurorehabilitation. Frontiers in Neuroscience. 10. 477–477. 12 indexed citations
11.
Hu, Tao & Liming Xiang. (2013). Efficient estimation for semiparametric cure models with interval-censored data. Journal of Multivariate Analysis. 121. 139–151. 30 indexed citations
12.
Burke, Linda, Andy H. Lee, Jonine Jancey, et al.. (2013). Physical activity and nutrition behavioural outcomes of a home-based intervention program for seniors: a randomized controlled trial. International Journal of Behavioral Nutrition and Physical Activity. 10(1). 14–14. 58 indexed citations
13.
Xiang, Liming, et al.. (2011). A Note on Tests for Zero-Inflation in Correlated Count Data. Communications in Statistics - Simulation and Computation. 40(7). 992–1005. 4 indexed citations
14.
Lee, Andy H. & Liming Xiang. (2011). Mixture Analysis of Heterogeneous Physical Activity Outcomes. Annals of Epidemiology. 21(10). 780–786. 4 indexed citations
15.
Lee, Andy H., Yun Zhao, Kelvin K.W. Yau, & Liming Xiang. (2010). How to analyze longitudinal multilevel physical activity data with many zeros?. Preventive Medicine. 51(6). 476–481. 19 indexed citations
16.
Tsung, Fugee, et al.. (2006). Improved design of proportional integral derivative charts. Quality Engineering. 51(5). 481–484. 3 indexed citations
17.
Xiang, Liming, Andy H. Lee, Kelvin K.W. Yau, & Geoffrey J. McLachlan. (2006). A score test for overdispersion in zero‐inflated poisson mixed regression model. Statistics in Medicine. 26(7). 1608–1622. 46 indexed citations
18.
Fung, Wing K., Hong Gu, Liming Xiang, & Kelvin K.W. Yau. (2006). Assessing local influence in principal component analysis with application to haematology study data. Statistics in Medicine. 26(13). 2730–2744. 4 indexed citations
19.
Xiang, Liming & Andy H. Lee. (2005). Sensitivity of Test for Overdispersion in Poisson Regression. Biometrical Journal. 47(2). 167–176. 11 indexed citations
20.
Lee, Andy H., Liming Xiang, & Wing K. Fung. (2004). Sensitivity of score tests for zero‐inflation in count data. Statistics in Medicine. 23(17). 2757–2769. 14 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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